Overview

Dataset statistics

Number of variables105
Number of observations16
Missing cells76
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.2 KiB
Average record size in memory848.0 B

Variable types

DateTime1
Numeric17
Categorical87

Alerts

dispute is highly correlated with total and 5 other fieldsHigh correlation
non_violent_crises is highly correlated with violent_crises and 8 other fieldsHigh correlation
violent_crises is highly correlated with non_violent_crises and 8 other fieldsHigh correlation
limited_wars is highly correlated with non_violent_crises and 4 other fieldsHigh correlation
wars is highly correlated with non_violent_crises and 6 other fieldsHigh correlation
total is highly correlated with dispute and 6 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] and 4 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] is highly correlated with dispute and 3 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] is highly correlated with non_violent_crises and 4 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] is highly correlated with dispute and 6 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] is highly correlated with limited_wars and 5 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] is highly correlated with non_violent_crises and 6 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] is highly correlated with dispute and 5 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] is highly correlated with dispute and 5 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] is highly correlated with dispute and 2 other fieldsHigh correlation
dispute is highly correlated with total and 5 other fieldsHigh correlation
non_violent_crises is highly correlated with violent_crises and 7 other fieldsHigh correlation
violent_crises is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
limited_wars is highly correlated with non_violent_crises and 3 other fieldsHigh correlation
wars is highly correlated with non_violent_crises and 8 other fieldsHigh correlation
total is highly correlated with dispute and 5 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] is highly correlated with wars and 5 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] is highly correlated with dispute and 4 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] is highly correlated with non_violent_crises and 3 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] is highly correlated with dispute and 2 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] is highly correlated with violent_crises and 8 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] is highly correlated with dispute and 9 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] is highly correlated with non_violent_crises and 4 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] is highly correlated with dispute and 9 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] is highly correlated with non_violent_crises and 8 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] is highly correlated with non_violent_crises and 8 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] is highly correlated with dispute and 2 other fieldsHigh correlation
non_violent_crises is highly correlated with violent_crises and 3 other fieldsHigh correlation
violent_crises is highly correlated with non_violent_crises and 5 other fieldsHigh correlation
limited_wars is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND]High correlation
wars is highly correlated with non_violent_crises and 4 other fieldsHigh correlation
total is highly correlated with violent_crises and 4 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] and 1 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] and 1 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] is highly correlated with violent_crises and 2 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA]High correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] and 1 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] is highly correlated with violent_crises and 4 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] is highly correlated with non_violent_crises and 1 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] is highly correlated with total and 3 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] is highly correlated with non_violent_crises and 4 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] is highly correlated with wars and 3 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA]High correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] is highly correlated with Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Germany [DEU] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 78 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Italy [ITA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 76 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 77 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - France [FRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 78 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 76 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 77 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - India [IND] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 76 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 77 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 78 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 77 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 75 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA] is highly correlated with GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] and 85 other fieldsHigh correlation
year is highly correlated with dispute and 103 other fieldsHigh correlation
dispute is highly correlated with year and 91 other fieldsHigh correlation
non_violent_crises is highly correlated with year and 93 other fieldsHigh correlation
violent_crises is highly correlated with year and 90 other fieldsHigh correlation
limited_wars is highly correlated with year and 90 other fieldsHigh correlation
wars is highly correlated with year and 90 other fieldsHigh correlation
total is highly correlated with year and 91 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN] is highly correlated with year and 103 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - India [IND] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN] is highly correlated with year and 103 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] is highly correlated with year and 94 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] is highly correlated with year and 96 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] is highly correlated with year and 96 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] is highly correlated with year and 92 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] is highly correlated with year and 92 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] is highly correlated with year and 94 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] is highly correlated with year and 96 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] is highly correlated with year and 95 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] is highly correlated with year and 98 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] is highly correlated with year and 95 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] is highly correlated with year and 91 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN] is highly correlated with year and 103 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD] is highly correlated with year and 103 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Germany [DEU] is highly correlated with year and 92 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - France [FRA] is highly correlated with year and 95 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Italy [ITA] is highly correlated with year and 96 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN] is highly correlated with year and 100 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN] is highly correlated with year and 102 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS] is highly correlated with year and 100 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA] is highly correlated with year and 100 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR] is highly correlated with year and 99 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA] is highly correlated with year and 103 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND] is highly correlated with year and 103 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] is highly correlated with year and 103 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF] is highly correlated with year and 103 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN] is highly correlated with year and 103 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN] is highly correlated with year and 95 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA] is highly correlated with year and 102 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN] is highly correlated with year and 103 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD] is highly correlated with year and 103 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU] has 5 (31.2%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA] has 5 (31.2%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA] has 5 (31.2%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN] has 5 (31.2%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN] has 5 (31.2%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS] has 6 (37.5%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA] has 5 (31.2%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR] has 5 (31.2%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA] has 6 (37.5%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND] has 6 (37.5%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX] has 5 (31.2%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF] has 6 (37.5%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN] has 6 (37.5%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD] has 6 (37.5%) missing values Missing
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - India [IND] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD] is uniformly distributed Uniform
Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA] is uniformly distributed Uniform
Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN] is uniformly distributed Uniform
Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD] is uniformly distributed Uniform
year has unique values Unique
violent_crises has unique values Unique
total has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - India [IND] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD] has unique values Unique

Reproduction

Analysis started2022-06-22 13:24:16.767719
Analysis finished2022-06-22 13:25:27.990791
Duration1 minute and 11.22 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

year
Date

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
Minimum1970-01-01 00:00:00.000002
Maximum1970-01-01 00:00:00.000002
2022-06-22T15:25:28.065167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:28.147431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)

dispute
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.375
Minimum63
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:28.228157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile66.75
Q170.5
median80.5
Q397.5
95-th percentile107
Maximum107
Range44
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.61996586
Coefficient of variation (CV)0.1851255213
Kurtosis-1.540707544
Mean84.375
Median Absolute Deviation (MAD)12.5
Skewness0.2549791868
Sum1350
Variance243.9833333
MonotonicityNot monotonic
2022-06-22T15:25:28.302103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1072
12.5%
682
12.5%
631
 
6.2%
721
 
6.2%
791
 
6.2%
821
 
6.2%
951
 
6.2%
1061
 
6.2%
991
 
6.2%
971
 
6.2%
Other values (4)4
25.0%
ValueCountFrequency (%)
631
6.2%
682
12.5%
691
6.2%
711
6.2%
721
6.2%
771
6.2%
791
6.2%
821
6.2%
901
6.2%
951
6.2%
ValueCountFrequency (%)
1072
12.5%
1061
6.2%
991
6.2%
971
6.2%
951
6.2%
901
6.2%
821
6.2%
791
6.2%
771
6.2%
721
6.2%

non_violent_crises
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.5
Minimum70
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:28.373402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile71.5
Q182.75
median88
Q3109.5
95-th percentile127
Maximum130
Range60
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation18.82197297
Coefficient of variation (CV)0.1991743172
Kurtosis-0.6739379008
Mean94.5
Median Absolute Deviation (MAD)8.5
Skewness0.7089213253
Sum1512
Variance354.2666667
MonotonicityNot monotonic
2022-06-22T15:25:28.453314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
882
 
12.5%
931
 
6.2%
1141
 
6.2%
1261
 
6.2%
1301
 
6.2%
1181
 
6.2%
1081
 
6.2%
871
 
6.2%
851
 
6.2%
821
 
6.2%
Other values (5)5
31.2%
ValueCountFrequency (%)
701
6.2%
721
6.2%
771
6.2%
821
6.2%
831
6.2%
851
6.2%
871
6.2%
882
12.5%
911
6.2%
931
6.2%
ValueCountFrequency (%)
1301
6.2%
1261
6.2%
1181
6.2%
1141
6.2%
1081
6.2%
931
6.2%
911
6.2%
882
12.5%
871
6.2%
851
6.2%

violent_crises
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.9375
Minimum90
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:28.531557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile99
Q1109.25
median165.5
Q3180.25
95-th percentile188.5
Maximum190
Range100
Interquartile range (IQR)71

Descriptive statistics

Standard deviation36.24080343
Coefficient of variation (CV)0.2401047018
Kurtosis-1.457733166
Mean150.9375
Median Absolute Deviation (MAD)20
Skewness-0.5732856418
Sum2415
Variance1313.395833
MonotonicityNot monotonic
2022-06-22T15:25:28.614247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
901
 
6.2%
1041
 
6.2%
1071
 
6.2%
1021
 
6.2%
1101
 
6.2%
1391
 
6.2%
1551
 
6.2%
1771
 
6.2%
1781
 
6.2%
1811
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
901
6.2%
1021
6.2%
1041
6.2%
1071
6.2%
1101
6.2%
1391
6.2%
1551
6.2%
1581
6.2%
1731
6.2%
1771
6.2%
ValueCountFrequency (%)
1901
6.2%
1881
6.2%
1831
6.2%
1811
6.2%
1801
6.2%
1781
6.2%
1771
6.2%
1731
6.2%
1581
6.2%
1551
6.2%

limited_wars
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.1875
Minimum16
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:28.687167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18.25
Q121.75
median25
Q326
95-th percentile30.25
Maximum31
Range15
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation4.16683333
Coefficient of variation (CV)0.172272179
Kurtosis-0.2284627711
Mean24.1875
Median Absolute Deviation (MAD)2.5
Skewness-0.1575500079
Sum387
Variance17.3625
MonotonicityNot monotonic
2022-06-22T15:25:28.764411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
254
25.0%
262
12.5%
302
12.5%
192
12.5%
221
 
6.2%
311
 
6.2%
241
 
6.2%
211
 
6.2%
161
 
6.2%
231
 
6.2%
ValueCountFrequency (%)
161
 
6.2%
192
12.5%
211
 
6.2%
221
 
6.2%
231
 
6.2%
241
 
6.2%
254
25.0%
262
12.5%
302
12.5%
311
 
6.2%
ValueCountFrequency (%)
311
 
6.2%
302
12.5%
262
12.5%
254
25.0%
241
 
6.2%
231
 
6.2%
221
 
6.2%
211
 
6.2%
192
12.5%
161
 
6.2%

wars
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:28.837901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17.5
median17
Q319.25
95-th percentile20.25
Maximum21
Range19
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation6.582805886
Coefficient of variation (CV)0.4702004204
Kurtosis-1.406969764
Mean14
Median Absolute Deviation (MAD)3
Skewness-0.5785301457
Sum224
Variance43.33333333
MonotonicityNot monotonic
2022-06-22T15:25:28.918599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
63
18.8%
203
18.8%
193
18.8%
21
 
6.2%
91
 
6.2%
81
 
6.2%
181
 
6.2%
161
 
6.2%
151
 
6.2%
211
 
6.2%
ValueCountFrequency (%)
21
 
6.2%
63
18.8%
81
 
6.2%
91
 
6.2%
151
 
6.2%
161
 
6.2%
181
 
6.2%
193
18.8%
203
18.8%
211
 
6.2%
ValueCountFrequency (%)
211
 
6.2%
203
18.8%
193
18.8%
181
 
6.2%
161
 
6.2%
151
 
6.2%
91
 
6.2%
81
 
6.2%
63
18.8%
21
 
6.2%

total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368
Minimum274
Maximum418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:28.998884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum274
5-th percentile313
Q1356.75
median369
Q3391.25
95-th percentile412
Maximum418
Range144
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation35.55090247
Coefficient of variation (CV)0.09660571324
Kurtosis2.194012998
Mean368
Median Absolute Deviation (MAD)17
Skewness-1.051035019
Sum5888
Variance1263.866667
MonotonicityNot monotonic
2022-06-22T15:25:29.086523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2741
 
6.2%
3261
 
6.2%
3441
 
6.2%
3531
 
6.2%
3681
 
6.2%
3701
 
6.2%
3871
 
6.2%
4051
 
6.2%
4181
 
6.2%
4101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2741
6.2%
3261
6.2%
3441
6.2%
3531
6.2%
3581
6.2%
3591
6.2%
3651
6.2%
3681
6.2%
3701
6.2%
3711
6.2%
ValueCountFrequency (%)
4181
6.2%
4101
6.2%
4051
6.2%
4041
6.2%
3871
6.2%
3761
6.2%
3711
6.2%
3701
6.2%
3681
6.2%
3651
6.2%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
0,731707164
 
1
3,816441913
 
1
2,976455131
 
1
0,959879134
 
1
-5,693836336
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11.125
Min length11

Characters and Unicode

Total characters178
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row0,731707164
2nd row3,816441913
3rd row2,976455131
4th row0,959879134
5th row-5,693836336

Common Values

ValueCountFrequency (%)
0,7317071641
 
6.2%
3,8164419131
 
6.2%
2,9764551311
 
6.2%
0,9598791341
 
6.2%
-5,6938363361
 
6.2%
4,1798824991
 
6.2%
3,9251927051
 
6.2%
0,4184975941
 
6.2%
0,4375913031
 
6.2%
2,2095434311
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.174531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,7317071641
 
6.2%
3,8164419131
 
6.2%
2,9764551311
 
6.2%
0,9598791341
 
6.2%
5,6938363361
 
6.2%
4,1798824991
 
6.2%
3,9251927051
 
6.2%
0,4184975941
 
6.2%
0,4375913031
 
6.2%
2,2095434311
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
124
13.5%
922
12.4%
419
10.7%
317
9.6%
,16
9.0%
515
8.4%
214
7.9%
013
7.3%
712
6.7%
612
6.7%
Other values (2)14
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160
89.9%
Other Punctuation16
 
9.0%
Dash Punctuation2
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
124
15.0%
922
13.8%
419
11.9%
317
10.6%
515
9.4%
214
8.8%
013
8.1%
712
7.5%
612
7.5%
812
7.5%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
124
13.5%
922
12.4%
419
10.7%
317
9.6%
,16
9.0%
515
8.4%
214
7.9%
013
7.3%
712
6.7%
612
6.7%
Other values (2)14
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124
13.5%
922
12.4%
419
10.7%
317
9.6%
,16
9.0%
515
8.4%
214
7.9%
013
7.3%
712
6.7%
612
6.7%
Other values (2)14
7.9%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,66321998
 
1
2,449323601
 
1
2,424736243
 
1
0,25494596
 
1
-2,873313828
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11
Min length10

Characters and Unicode

Total characters176
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,66321998
2nd row2,449323601
3rd row2,424736243
4th row0,25494596
5th row-2,873313828

Common Values

ValueCountFrequency (%)
1,663219981
 
6.2%
2,4493236011
 
6.2%
2,4247362431
 
6.2%
0,254945961
 
6.2%
-2,8733138281
 
6.2%
1,9494376231
 
6.2%
2,1927006331
 
6.2%
0,3131347511
 
6.2%
0,5763266751
 
6.2%
0,9561830521
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.262191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,663219981
 
6.2%
2,4493236011
 
6.2%
2,4247362431
 
6.2%
0,254945961
 
6.2%
2,8733138281
 
6.2%
1,9494376231
 
6.2%
2,1927006331
 
6.2%
0,3131347511
 
6.2%
0,5763266751
 
6.2%
0,9561830521
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
123
13.1%
220
11.4%
319
10.8%
419
10.8%
,16
9.1%
616
9.1%
916
9.1%
013
7.4%
512
6.8%
711
6.2%
Other values (2)11
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158
89.8%
Other Punctuation16
 
9.1%
Dash Punctuation2
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123
14.6%
220
12.7%
319
12.0%
419
12.0%
616
10.1%
916
10.1%
013
8.2%
512
7.6%
711
7.0%
89
 
5.7%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
123
13.1%
220
11.4%
319
10.8%
419
10.8%
,16
9.1%
616
9.1%
916
9.1%
013
7.4%
512
6.8%
711
6.2%
Other values (2)11
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
13.1%
220
11.4%
319
10.8%
419
10.8%
,16
9.1%
616
9.1%
916
9.1%
013
7.4%
512
6.8%
711
6.2%
Other values (2)11
6.2%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
0,817848974
 
1
1,790639681
 
1
1,48707298
 
1
-0,962012841
 
1
-5,280937208
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11.1875
Min length10

Characters and Unicode

Total characters179
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row0,817848974
2nd row1,790639681
3rd row1,48707298
4th row-0,962012841
5th row-5,280937208

Common Values

ValueCountFrequency (%)
0,8178489741
 
6.2%
1,7906396811
 
6.2%
1,487072981
 
6.2%
-0,9620128411
 
6.2%
-5,2809372081
 
6.2%
1,7132958391
 
6.2%
0,7073333471
 
6.2%
-2,9809057681
 
6.2%
-1,8410654511
 
6.2%
-0,0045475421
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.352871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,8178489741
 
6.2%
1,7906396811
 
6.2%
1,487072981
 
6.2%
0,9620128411
 
6.2%
5,2809372081
 
6.2%
1,7132958391
 
6.2%
0,7073333471
 
6.2%
2,9809057681
 
6.2%
1,8410654511
 
6.2%
0,0045475421
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
022
12.3%
819
10.6%
118
10.1%
717
9.5%
917
9.5%
,16
8.9%
416
8.9%
215
8.4%
313
7.3%
512
6.7%
Other values (2)14
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number157
87.7%
Other Punctuation16
 
8.9%
Dash Punctuation6
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
022
14.0%
819
12.1%
118
11.5%
717
10.8%
917
10.8%
416
10.2%
215
9.6%
313
8.3%
512
7.6%
68
 
5.1%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common179
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
022
12.3%
819
10.6%
118
10.1%
717
9.5%
917
9.5%
,16
8.9%
416
8.9%
215
8.4%
313
7.3%
512
6.7%
Other values (2)14
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII179
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
022
12.3%
819
10.6%
118
10.1%
717
9.5%
917
9.5%
,16
8.9%
416
8.9%
215
8.4%
313
7.3%
512
6.7%
Other values (2)14
7.8%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,803900872
 
1
1,372350128
 
1
1,483969412
 
1
-1,224289001
 
1
-5,693236359
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11.1875
Min length11

Characters and Unicode

Total characters179
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,803900872
2nd row1,372350128
3rd row1,483969412
4th row-1,224289001
5th row-5,693236359

Common Values

ValueCountFrequency (%)
1,8039008721
 
6.2%
1,3723501281
 
6.2%
1,4839694121
 
6.2%
-1,2242890011
 
6.2%
-5,6932363591
 
6.2%
4,0979179191
 
6.2%
0,0238095241
 
6.2%
1,3747509991
 
6.2%
2,0051001771
 
6.2%
0,2962055141
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.438574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,8039008721
 
6.2%
1,3723501281
 
6.2%
1,4839694121
 
6.2%
1,2242890011
 
6.2%
5,6932363591
 
6.2%
4,0979179191
 
6.2%
0,0238095241
 
6.2%
1,3747509991
 
6.2%
2,0051001771
 
6.2%
0,2962055141
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
024
13.4%
520
11.2%
219
10.6%
118
10.1%
,16
8.9%
916
8.9%
715
8.4%
313
7.3%
812
6.7%
612
6.7%
Other values (2)14
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160
89.4%
Other Punctuation16
 
8.9%
Dash Punctuation3
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024
15.0%
520
12.5%
219
11.9%
118
11.2%
916
10.0%
715
9.4%
313
8.1%
812
7.5%
612
7.5%
411
6.9%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common179
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024
13.4%
520
11.2%
219
10.6%
118
10.1%
,16
8.9%
916
8.9%
715
8.4%
313
7.3%
812
6.7%
612
6.7%
Other values (2)14
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII179
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024
13.4%
520
11.2%
219
10.6%
118
10.1%
,16
8.9%
916
8.9%
715
8.4%
313
7.3%
812
6.7%
612
6.7%
Other values (2)14
7.8%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
4,995860869
 
1
4,165817627
 
1
6,868608857
 
1
1,007622695
 
1
-2,928400167
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11.0625
Min length10

Characters and Unicode

Total characters177
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row4,995860869
2nd row4,165817627
3rd row6,868608857
4th row1,007622695
5th row-2,928400167

Common Values

ValueCountFrequency (%)
4,9958608691
 
6.2%
4,1658176271
 
6.2%
6,8686088571
 
6.2%
1,0076226951
 
6.2%
-2,9284001671
 
6.2%
3,089494621
 
6.2%
3,1468813721
 
6.2%
1,7622225491
 
6.2%
2,3291225061
 
6.2%
2,8700360751
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.526767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,9958608691
 
6.2%
4,1658176271
 
6.2%
6,8686088571
 
6.2%
1,0076226951
 
6.2%
2,9284001671
 
6.2%
3,089494621
 
6.2%
3,1468813721
 
6.2%
1,7622225491
 
6.2%
2,3291225061
 
6.2%
2,8700360751
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
223
13.0%
021
11.9%
618
10.2%
817
9.6%
,16
9.0%
414
7.9%
914
7.9%
714
7.9%
513
7.3%
113
7.3%
Other values (2)14
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
89.8%
Other Punctuation16
 
9.0%
Dash Punctuation2
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
223
14.5%
021
13.2%
618
11.3%
817
10.7%
414
8.8%
914
8.8%
714
8.8%
513
8.2%
113
8.2%
312
7.5%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
223
13.0%
021
11.9%
618
10.2%
817
9.6%
,16
9.0%
414
7.9%
914
7.9%
714
7.9%
513
7.3%
113
7.3%
Other values (2)14
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
223
13.0%
021
11.9%
618
10.2%
817
9.6%
,16
9.0%
414
7.9%
914
7.9%
714
7.9%
513
7.3%
113
7.3%
Other values (2)14
7.9%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
6,399965448
 
1
8,200068255
 
1
8,499977768
 
1
5,199969265
 
1
-7,799993913
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length10.625
Min length3

Characters and Unicode

Total characters170
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row6,399965448
2nd row8,200068255
3rd row8,499977768
4th row5,199969265
5th row-7,799993913

Common Values

ValueCountFrequency (%)
6,3999654481
 
6.2%
8,2000682551
 
6.2%
8,4999777681
 
6.2%
5,1999692651
 
6.2%
-7,7999939131
 
6.2%
4,51
 
6.2%
4,3000291861
 
6.2%
4,0240861571
 
6.2%
1,7554221491
 
6.2%
0,7362672211
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.618949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6,3999654481
 
6.2%
8,2000682551
 
6.2%
8,4999777681
 
6.2%
5,1999692651
 
6.2%
7,7999939131
 
6.2%
4,51
 
6.2%
4,3000291861
 
6.2%
4,0240861571
 
6.2%
1,7554221491
 
6.2%
0,7362672211
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
927
15.9%
222
12.9%
,16
9.4%
016
9.4%
716
9.4%
613
7.6%
113
7.6%
512
7.1%
412
7.1%
811
6.5%
Other values (2)12
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number151
88.8%
Other Punctuation16
 
9.4%
Dash Punctuation3
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
927
17.9%
222
14.6%
016
10.6%
716
10.6%
613
8.6%
113
8.6%
512
7.9%
412
7.9%
811
7.3%
39
 
6.0%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
927
15.9%
222
12.9%
,16
9.4%
016
9.4%
716
9.4%
613
7.6%
113
7.6%
512
7.1%
412
7.1%
811
6.5%
Other values (2)12
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
927
15.9%
222
12.9%
,16
9.4%
016
9.4%
716
9.4%
613
7.6%
113
7.6%
512
7.1%
412
7.1%
811
6.5%
Other values (2)12
7.1%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
3,513213799
 
1
2,854972294
 
1
1,876171454
 
1
-0,136579803
 
1
-2,536757067
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11.0625
Min length10

Characters and Unicode

Total characters177
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row3,513213799
2nd row2,854972294
3rd row1,876171454
4th row-0,136579803
5th row-2,536757067

Common Values

ValueCountFrequency (%)
3,5132137991
 
6.2%
2,8549722941
 
6.2%
1,8761714541
 
6.2%
-0,1365798031
 
6.2%
-2,5367570671
 
6.2%
2,563766561
 
6.2%
1,5508355051
 
6.2%
2,2495458521
 
6.2%
1,8420810711
 
6.2%
2,5259734461
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.709227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,5132137991
 
6.2%
2,8549722941
 
6.2%
1,8761714541
 
6.2%
0,1365798031
 
6.2%
2,5367570671
 
6.2%
2,563766561
 
6.2%
1,5508355051
 
6.2%
2,2495458521
 
6.2%
1,8420810711
 
6.2%
2,5259734461
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
527
15.3%
119
10.7%
218
10.2%
718
10.2%
316
9.0%
,16
9.0%
416
9.0%
616
9.0%
912
6.8%
09
 
5.1%
Other values (2)10
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158
89.3%
Other Punctuation16
 
9.0%
Dash Punctuation3
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
527
17.1%
119
12.0%
218
11.4%
718
11.4%
316
10.1%
416
10.1%
616
10.1%
912
7.6%
09
 
5.7%
87
 
4.4%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
527
15.3%
119
10.7%
218
10.2%
718
10.2%
316
9.0%
,16
9.0%
416
9.0%
616
9.0%
912
6.8%
09
 
5.1%
Other values (2)10
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
527
15.3%
119
10.7%
218
10.2%
718
10.2%
316
9.0%
,16
9.0%
416
9.0%
616
9.0%
912
6.8%
09
 
5.1%
Other values (2)10
 
5.6%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,59328001
 
1
2,584104677
 
1
2,269486875
 
1
-0,239638085
 
1
-4,247356266
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11.125
Min length10

Characters and Unicode

Total characters178
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,59328001
2nd row2,584104677
3rd row2,269486875
4th row-0,239638085
5th row-4,247356266

Common Values

ValueCountFrequency (%)
2,593280011
 
6.2%
2,5841046771
 
6.2%
2,2694868751
 
6.2%
-0,2396380851
 
6.2%
-4,2473562661
 
6.2%
2,1314381981
 
6.2%
1,4575633911
 
6.2%
1,4698875211
 
6.2%
1,8900183421
 
6.2%
2,9911648141
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.800473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,593280011
 
6.2%
2,5841046771
 
6.2%
2,2694868751
 
6.2%
0,2396380851
 
6.2%
4,2473562661
 
6.2%
2,1314381981
 
6.2%
1,4575633911
 
6.2%
1,4698875211
 
6.2%
1,8900183421
 
6.2%
2,9911648141
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
621
11.8%
120
11.2%
219
10.7%
918
10.1%
417
9.6%
,16
9.0%
316
9.0%
814
7.9%
513
7.3%
012
6.7%
Other values (2)12
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
89.3%
Other Punctuation16
 
9.0%
Dash Punctuation3
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
621
13.2%
120
12.6%
219
11.9%
918
11.3%
417
10.7%
316
10.1%
814
8.8%
513
8.2%
012
7.5%
79
5.7%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
621
11.8%
120
11.2%
219
10.7%
918
10.1%
417
9.6%
,16
9.0%
316
9.0%
814
7.9%
513
7.3%
012
6.7%
Other values (2)12
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
621
11.8%
120
11.2%
219
10.7%
918
10.1%
417
9.6%
,16
9.0%
316
9.0%
814
7.9%
513
7.3%
012
6.7%
Other values (2)12
6.7%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
3,202132062
 
1
3,961988709
 
1
6,069870607
 
1
5,094195448
 
1
-0,125812003
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11.125
Min length10

Characters and Unicode

Total characters178
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row3,202132062
2nd row3,961988709
3rd row6,069870607
4th row5,094195448
5th row-0,125812003

Common Values

ValueCountFrequency (%)
3,2021320621
 
6.2%
3,9619887091
 
6.2%
6,0698706071
 
6.2%
5,0941954481
 
6.2%
-0,1258120031
 
6.2%
7,5282258181
 
6.2%
3,9744230791
 
6.2%
1,9211759851
 
6.2%
3,004822671
 
6.2%
0,503955741
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.893255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,2021320621
 
6.2%
3,9619887091
 
6.2%
6,0698706071
 
6.2%
5,0941954481
 
6.2%
0,1258120031
 
6.2%
7,5282258181
 
6.2%
3,9744230791
 
6.2%
1,9211759851
 
6.2%
3,004822671
 
6.2%
0,503955741
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
019
10.7%
218
10.1%
117
9.6%
917
9.6%
517
9.6%
316
9.0%
,16
9.0%
614
7.9%
814
7.9%
714
7.9%
Other values (2)16
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158
88.8%
Other Punctuation16
 
9.0%
Dash Punctuation4
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019
12.0%
218
11.4%
117
10.8%
917
10.8%
517
10.8%
316
10.1%
614
8.9%
814
8.9%
714
8.9%
412
7.6%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019
10.7%
218
10.1%
117
9.6%
917
9.6%
517
9.6%
316
9.0%
,16
9.0%
614
7.9%
814
7.9%
714
7.9%
Other values (2)16
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019
10.7%
218
10.1%
117
9.6%
917
9.6%
517
9.6%
316
9.0%
,16
9.0%
614
7.9%
814
7.9%
714
7.9%
Other values (2)16
9.0%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
7,923430621
 
1
8,060732573
 
1
7,660815065
 
1
3,08669806
 
1
7,861888833
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11
Min length10

Characters and Unicode

Total characters176
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row7,923430621
2nd row8,060732573
3rd row7,660815065
4th row3,08669806
5th row7,861888833

Common Values

ValueCountFrequency (%)
7,9234306211
 
6.2%
8,0607325731
 
6.2%
7,6608150651
 
6.2%
3,086698061
 
6.2%
7,8618888331
 
6.2%
8,4975847021
 
6.2%
5,2413150011
 
6.2%
5,4563887531
 
6.2%
6,3861064011
 
6.2%
7,4102276051
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:29.988829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7,9234306211
 
6.2%
8,0607325731
 
6.2%
7,6608150651
 
6.2%
3,086698061
 
6.2%
7,8618888331
 
6.2%
8,4975847021
 
6.2%
5,2413150011
 
6.2%
5,4563887531
 
6.2%
6,3861064011
 
6.2%
7,4102276051
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
820
11.4%
520
11.4%
619
10.8%
018
10.2%
717
9.7%
,16
9.1%
316
9.1%
115
8.5%
213
7.4%
412
6.8%
Other values (2)10
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
90.3%
Other Punctuation16
 
9.1%
Dash Punctuation1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
820
12.6%
520
12.6%
619
11.9%
018
11.3%
717
10.7%
316
10.1%
115
9.4%
213
8.2%
412
7.5%
99
5.7%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
820
11.4%
520
11.4%
619
10.8%
018
10.2%
717
9.7%
,16
9.1%
316
9.1%
115
8.5%
213
7.4%
412
6.8%
Other values (2)10
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
820
11.4%
520
11.4%
619
10.8%
018
10.2%
717
9.7%
,16
9.1%
316
9.1%
115
8.5%
213
7.4%
412
6.8%
Other values (2)10
5.7%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,307807066
 
1
4,495077894
 
1
2,291445714
 
1
1,143584587
 
1
-5,285744137
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11.125
Min length10

Characters and Unicode

Total characters178
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,307807066
2nd row4,495077894
3rd row2,291445714
4th row1,143584587
5th row-5,285744137

Common Values

ValueCountFrequency (%)
2,3078070661
 
6.2%
4,4950778941
 
6.2%
2,2914457141
 
6.2%
1,1435845871
 
6.2%
-5,2857441371
 
6.2%
5,1181181431
 
6.2%
3,663007931
 
6.2%
3,6423226791
 
6.2%
1,3540919621
 
6.2%
2,8497732551
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.081587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,3078070661
 
6.2%
4,4950778941
 
6.2%
2,2914457141
 
6.2%
1,1435845871
 
6.2%
5,2857441371
 
6.2%
5,1181181431
 
6.2%
3,663007931
 
6.2%
3,6423226791
 
6.2%
1,3540919621
 
6.2%
2,8497732551
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
120
11.2%
219
10.7%
319
10.7%
419
10.7%
519
10.7%
917
9.6%
,16
9.0%
714
7.9%
011
6.2%
611
6.2%
Other values (2)13
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
89.3%
Other Punctuation16
 
9.0%
Dash Punctuation3
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
120
12.6%
219
11.9%
319
11.9%
419
11.9%
519
11.9%
917
10.7%
714
8.8%
011
6.9%
611
6.9%
810
6.3%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
120
11.2%
219
10.7%
319
10.7%
419
10.7%
519
10.7%
917
9.6%
,16
9.0%
714
7.9%
011
6.2%
611
6.2%
Other values (2)13
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
11.2%
219
10.7%
319
10.7%
419
10.7%
519
10.7%
917
9.6%
,16
9.0%
714
7.9%
011
6.2%
611
6.2%
Other values (2)13
7.3%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
5,277051973
 
1
5,603806459
 
1
5,360474054
 
1
3,191043886
 
1
-1,538089135
 
1
Other values (11)
11 

Length

Max length12
Median length11
Mean length11.125
Min length11

Characters and Unicode

Total characters178
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row5,277051973
2nd row5,603806459
3rd row5,360474054
4th row3,191043886
5th row-1,538089135

Common Values

ValueCountFrequency (%)
5,2770519731
 
6.2%
5,6038064591
 
6.2%
5,3604740541
 
6.2%
3,1910438861
 
6.2%
-1,5380891351
 
6.2%
3,0397328811
 
6.2%
3,1685562791
 
6.2%
2,3962323851
 
6.2%
2,4854680081
 
6.2%
1,4138264521
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.168601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,2770519731
 
6.2%
5,6038064591
 
6.2%
5,3604740541
 
6.2%
3,1910438861
 
6.2%
1,5380891351
 
6.2%
3,0397328811
 
6.2%
3,1685562791
 
6.2%
2,3962323851
 
6.2%
2,4854680081
 
6.2%
1,4138264521
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
323
12.9%
518
10.1%
118
10.1%
617
9.6%
817
9.6%
,16
9.0%
215
8.4%
014
7.9%
414
7.9%
713
7.3%
Other values (2)13
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160
89.9%
Other Punctuation16
 
9.0%
Dash Punctuation2
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
323
14.4%
518
11.2%
118
11.2%
617
10.6%
817
10.6%
215
9.4%
014
8.8%
414
8.8%
713
8.1%
911
6.9%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
323
12.9%
518
10.1%
118
10.1%
617
9.6%
817
9.6%
,16
9.0%
215
8.4%
014
7.9%
414
7.9%
713
7.3%
Other values (2)13
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
323
12.9%
518
10.1%
118
10.1%
617
9.6%
817
9.6%
,16
9.0%
215
8.4%
014
7.9%
414
7.9%
713
7.3%
Other values (2)13
7.3%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
11,39459181
 
1
12,72095567
 
1
14,23086093
 
1
9,65067892
 
1
9,398725632
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row11,39459181
2nd row12,72095567
3rd row14,23086093
4th row9,65067892
5th row9,398725632

Common Values

ValueCountFrequency (%)
11,394591811
 
6.2%
12,720955671
 
6.2%
14,230860931
 
6.2%
9,650678921
 
6.2%
9,3987256321
 
6.2%
10,635871061
 
6.2%
9,5508321791
 
6.2%
7,8637364491
 
6.2%
7,7661500981
 
6.2%
7,4257636561
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.253692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11,394591811
 
6.2%
12,720955671
 
6.2%
14,230860931
 
6.2%
9,650678921
 
6.2%
9,3987256321
 
6.2%
10,635871061
 
6.2%
9,5508321791
 
6.2%
7,8637364491
 
6.2%
7,7661500981
 
6.2%
7,4257636561
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
723
13.1%
319
10.9%
918
10.3%
618
10.3%
,16
9.1%
515
8.6%
215
8.6%
113
7.4%
813
7.4%
013
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
723
14.5%
319
11.9%
918
11.3%
618
11.3%
515
9.4%
215
9.4%
113
8.2%
813
8.2%
013
8.2%
412
7.5%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
723
13.1%
319
10.9%
918
10.3%
618
10.3%
,16
9.1%
515
8.6%
215
8.6%
113
7.4%
813
7.4%
013
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
723
13.1%
319
10.9%
918
10.3%
618
10.3%
,16
9.1%
515
8.6%
215
8.6%
113
7.4%
813
7.4%
013
7.4%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
4,048174008
 
1
4,495625664
 
1
4,438754307
 
1
2,000830512
 
1
-1,307257
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length9

Characters and Unicode

Total characters174
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row4,048174008
2nd row4,495625664
3rd row4,438754307
4th row2,000830512
5th row-1,307257

Common Values

ValueCountFrequency (%)
4,0481740081
 
6.2%
4,4956256641
 
6.2%
4,4387543071
 
6.2%
2,0008305121
 
6.2%
-1,3072571
 
6.2%
4,4946945691
 
6.2%
3,3396178861
 
6.2%
2,6728184111
 
6.2%
2,8446516331
 
6.2%
3,1177528171
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.350351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,0481740081
 
6.2%
4,4956256641
 
6.2%
4,4387543071
 
6.2%
2,0008305121
 
6.2%
1,3072571
 
6.2%
4,4946945691
 
6.2%
3,3396178861
 
6.2%
2,6728184111
 
6.2%
2,8446516331
 
6.2%
3,1177528171
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
425
14.4%
322
12.6%
818
10.3%
,16
9.2%
216
9.2%
615
8.6%
014
8.0%
114
8.0%
713
7.5%
511
6.3%
Other values (2)10
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number156
89.7%
Other Punctuation16
 
9.2%
Dash Punctuation2
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
425
16.0%
322
14.1%
818
11.5%
216
10.3%
615
9.6%
014
9.0%
114
9.0%
713
8.3%
511
7.1%
98
 
5.1%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
425
14.4%
322
12.6%
818
10.3%
,16
9.2%
216
9.2%
615
8.6%
014
8.0%
114
8.0%
713
7.5%
511
6.3%
Other values (2)10
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
425
14.4%
322
12.6%
818
10.3%
,16
9.2%
216
9.2%
615
8.6%
014
8.0%
114
8.0%
713
7.5%
511
6.3%
Other values (2)10
 
5.7%

GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,84686E+12
 
1
2,9947E+12
 
1
3,42558E+12
 
1
3,74526E+12
 
1
3,41126E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters174
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,84686E+12
2nd row2,9947E+12
3rd row3,42558E+12
4th row3,74526E+12
5th row3,41126E+12

Common Values

ValueCountFrequency (%)
2,84686E+121
 
6.2%
2,9947E+121
 
6.2%
3,42558E+121
 
6.2%
3,74526E+121
 
6.2%
3,41126E+121
 
6.2%
3,39967E+121
 
6.2%
3,74931E+121
 
6.2%
3,52714E+121
 
6.2%
3,7338E+121
 
6.2%
3,88909E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.447434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,84686e+121
 
6.2%
2,9947e+121
 
6.2%
3,42558e+121
 
6.2%
3,74526e+121
 
6.2%
3,41126e+121
 
6.2%
3,39967e+121
 
6.2%
3,74931e+121
 
6.2%
3,52714e+121
 
6.2%
3,7338e+121
 
6.2%
3,88909e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
223
13.2%
121
12.1%
321
12.1%
,16
9.2%
E16
9.2%
+16
9.2%
812
6.9%
912
6.9%
410
5.7%
79
 
5.2%
Other values (3)18
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number126
72.4%
Other Punctuation16
 
9.2%
Uppercase Letter16
 
9.2%
Math Symbol16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
223
18.3%
121
16.7%
321
16.7%
812
9.5%
912
9.5%
410
7.9%
79
 
7.1%
68
 
6.3%
58
 
6.3%
02
 
1.6%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common158
90.8%
Latin16
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
223
14.6%
121
13.3%
321
13.3%
,16
10.1%
+16
10.1%
812
7.6%
912
7.6%
410
6.3%
79
 
5.7%
68
 
5.1%
Other values (2)10
6.3%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
223
13.2%
121
12.1%
321
12.1%
,16
9.2%
E16
9.2%
+16
9.2%
812
6.9%
912
6.9%
410
5.7%
79
 
5.2%
Other values (3)18
10.3%

GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,19695E+12
 
1
2,32054E+12
 
1
2,66059E+12
 
1
2,9303E+12
 
1
2,70089E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,19695E+12
2nd row2,32054E+12
3rd row2,66059E+12
4th row2,9303E+12
5th row2,70089E+12

Common Values

ValueCountFrequency (%)
2,19695E+121
 
6.2%
2,32054E+121
 
6.2%
2,66059E+121
 
6.2%
2,9303E+121
 
6.2%
2,70089E+121
 
6.2%
2,64519E+121
 
6.2%
2,86516E+121
 
6.2%
2,68367E+121
 
6.2%
2,81188E+121
 
6.2%
2,85596E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.539416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,19695e+121
 
6.2%
2,32054e+121
 
6.2%
2,66059e+121
 
6.2%
2,9303e+121
 
6.2%
2,70089e+121
 
6.2%
2,64519e+121
 
6.2%
2,86516e+121
 
6.2%
2,68367e+121
 
6.2%
2,81188e+121
 
6.2%
2,85596e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
236
20.6%
123
13.1%
,16
9.1%
E16
9.1%
+16
9.1%
913
 
7.4%
612
 
6.9%
510
 
5.7%
89
 
5.1%
37
 
4.0%
Other values (3)17
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number127
72.6%
Other Punctuation16
 
9.1%
Uppercase Letter16
 
9.1%
Math Symbol16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
236
28.3%
123
18.1%
913
 
10.2%
612
 
9.4%
510
 
7.9%
89
 
7.1%
37
 
5.5%
07
 
5.5%
76
 
4.7%
44
 
3.1%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common159
90.9%
Latin16
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
236
22.6%
123
14.5%
,16
10.1%
+16
10.1%
913
 
8.2%
612
 
7.5%
510
 
6.3%
89
 
5.7%
37
 
4.4%
07
 
4.4%
Other values (2)10
 
6.3%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
236
20.6%
123
13.1%
,16
9.1%
E16
9.1%
+16
9.1%
913
 
7.4%
612
 
6.9%
510
 
5.7%
89
 
5.1%
37
 
4.0%
Other values (3)17
9.7%

GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,85822E+12
 
1
1,94955E+12
 
1
2,2131E+12
 
1
2,40866E+12
 
1
2,19993E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.8125
Min length10

Characters and Unicode

Total characters173
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,85822E+12
2nd row1,94955E+12
3rd row2,2131E+12
4th row2,40866E+12
5th row2,19993E+12

Common Values

ValueCountFrequency (%)
1,85822E+121
 
6.2%
1,94955E+121
 
6.2%
2,2131E+121
 
6.2%
2,40866E+121
 
6.2%
2,19993E+121
 
6.2%
2,1361E+121
 
6.2%
2,29499E+121
 
6.2%
2,08696E+121
 
6.2%
2,14192E+121
 
6.2%
2,16201E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.640357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,85822e+121
 
6.2%
1,94955e+121
 
6.2%
2,2131e+121
 
6.2%
2,40866e+121
 
6.2%
2,19993e+121
 
6.2%
2,1361e+121
 
6.2%
2,29499e+121
 
6.2%
2,08696e+121
 
6.2%
2,14192e+121
 
6.2%
2,16201e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
134
19.7%
232
18.5%
,16
9.2%
E16
9.2%
+16
9.2%
914
8.1%
811
 
6.4%
69
 
5.2%
07
 
4.0%
36
 
3.5%
Other values (3)12
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125
72.3%
Other Punctuation16
 
9.2%
Uppercase Letter16
 
9.2%
Math Symbol16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
134
27.2%
232
25.6%
914
11.2%
811
 
8.8%
69
 
7.2%
07
 
5.6%
36
 
4.8%
45
 
4.0%
74
 
3.2%
53
 
2.4%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common157
90.8%
Latin16
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
134
21.7%
232
20.4%
,16
10.2%
+16
10.2%
914
8.9%
811
 
7.0%
69
 
5.7%
07
 
4.5%
36
 
3.8%
45
 
3.2%
Other values (2)7
 
4.5%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
19.7%
232
18.5%
,16
9.2%
E16
9.2%
+16
9.2%
914
8.1%
811
 
6.4%
69
 
5.2%
07
 
4.0%
36
 
3.5%
Other values (3)12
 
6.9%

GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
4,83147E+12
 
1
4,60166E+12
 
1
4,57975E+12
 
1
5,10668E+12
 
1
5,28949E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters176
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row4,83147E+12
2nd row4,60166E+12
3rd row4,57975E+12
4th row5,10668E+12
5th row5,28949E+12

Common Values

ValueCountFrequency (%)
4,83147E+121
 
6.2%
4,60166E+121
 
6.2%
4,57975E+121
 
6.2%
5,10668E+121
 
6.2%
5,28949E+121
 
6.2%
5,75907E+121
 
6.2%
6,23315E+121
 
6.2%
6,27236E+121
 
6.2%
5,21233E+121
 
6.2%
4,89699E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.734785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,83147e+121
 
6.2%
4,60166e+121
 
6.2%
4,57975e+121
 
6.2%
5,10668e+121
 
6.2%
5,28949e+121
 
6.2%
5,75907e+121
 
6.2%
6,23315e+121
 
6.2%
6,27236e+121
 
6.2%
5,21233e+121
 
6.2%
4,89699e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
122
12.5%
222
12.5%
,16
9.1%
E16
9.1%
+16
9.1%
413
7.4%
513
7.4%
612
6.8%
310
 
5.7%
910
 
5.7%
Other values (3)26
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number128
72.7%
Other Punctuation16
 
9.1%
Uppercase Letter16
 
9.1%
Math Symbol16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
122
17.2%
222
17.2%
413
10.2%
513
10.2%
612
9.4%
310
7.8%
910
7.8%
89
7.0%
79
7.0%
08
 
6.2%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common160
90.9%
Latin16
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
122
13.8%
222
13.8%
,16
10.0%
+16
10.0%
413
8.1%
513
8.1%
612
7.5%
310
6.2%
910
6.2%
89
5.6%
Other values (2)17
10.6%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122
12.5%
222
12.5%
,16
9.1%
E16
9.1%
+16
9.1%
413
7.4%
513
7.4%
612
6.8%
310
 
5.7%
910
 
5.7%
Other values (3)26
14.8%

GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,17311E+12
 
1
1,31926E+12
 
1
1,46882E+12
 
1
1,55299E+12
 
1
1,37463E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,17311E+12
2nd row1,31926E+12
3rd row1,46882E+12
4th row1,55299E+12
5th row1,37463E+12

Common Values

ValueCountFrequency (%)
1,17311E+121
 
6.2%
1,31926E+121
 
6.2%
1,46882E+121
 
6.2%
1,55299E+121
 
6.2%
1,37463E+121
 
6.2%
1,61734E+121
 
6.2%
1,79333E+121
 
6.2%
1,82837E+121
 
6.2%
1,8466E+121
 
6.2%
1,80575E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.827031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,17311e+121
 
6.2%
1,31926e+121
 
6.2%
1,46882e+121
 
6.2%
1,55299e+121
 
6.2%
1,37463e+121
 
6.2%
1,61734e+121
 
6.2%
1,79333e+121
 
6.2%
1,82837e+121
 
6.2%
1,8466e+121
 
6.2%
1,80575e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
138
21.7%
226
14.9%
,16
9.1%
E16
9.1%
+16
9.1%
311
 
6.3%
710
 
5.7%
510
 
5.7%
69
 
5.1%
48
 
4.6%
Other values (3)15
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number127
72.6%
Other Punctuation16
 
9.1%
Uppercase Letter16
 
9.1%
Math Symbol16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
138
29.9%
226
20.5%
311
 
8.7%
710
 
7.9%
510
 
7.9%
69
 
7.1%
48
 
6.3%
97
 
5.5%
86
 
4.7%
02
 
1.6%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common159
90.9%
Latin16
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
138
23.9%
226
16.4%
,16
10.1%
+16
10.1%
311
 
6.9%
710
 
6.3%
510
 
6.3%
69
 
5.7%
48
 
5.0%
97
 
4.4%
Other values (2)8
 
5.0%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
21.7%
226
14.9%
,16
9.1%
E16
9.1%
+16
9.1%
311
 
6.3%
710
 
5.7%
510
 
5.7%
69
 
5.1%
48
 
4.6%
Other values (3)15
 
8.6%

GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
7,64017E+11
 
1
9,89931E+11
 
1
1,29971E+12
 
1
1,66085E+12
 
1
1,22264E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.8125
Min length10

Characters and Unicode

Total characters173
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row7,64017E+11
2nd row9,89931E+11
3rd row1,29971E+12
4th row1,66085E+12
5th row1,22264E+12

Common Values

ValueCountFrequency (%)
7,64017E+111
 
6.2%
9,89931E+111
 
6.2%
1,29971E+121
 
6.2%
1,66085E+121
 
6.2%
1,22264E+121
 
6.2%
1,52492E+121
 
6.2%
2,04593E+121
 
6.2%
2,2083E+121
 
6.2%
2,29247E+121
 
6.2%
2,05924E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:30.918285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7,64017e+111
 
6.2%
9,89931e+111
 
6.2%
1,29971e+121
 
6.2%
1,66085e+121
 
6.2%
1,22264e+121
 
6.2%
1,52492e+121
 
6.2%
2,04593e+121
 
6.2%
2,2083e+121
 
6.2%
2,29247e+121
 
6.2%
2,05924e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
131
17.9%
230
17.3%
,16
9.2%
E16
9.2%
+16
9.2%
410
 
5.8%
910
 
5.8%
79
 
5.2%
68
 
4.6%
38
 
4.6%
Other values (3)19
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125
72.3%
Other Punctuation16
 
9.2%
Uppercase Letter16
 
9.2%
Math Symbol16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
131
24.8%
230
24.0%
410
 
8.0%
910
 
8.0%
79
 
7.2%
68
 
6.4%
38
 
6.4%
58
 
6.4%
86
 
4.8%
05
 
4.0%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common157
90.8%
Latin16
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
131
19.7%
230
19.1%
,16
10.2%
+16
10.2%
410
 
6.4%
910
 
6.4%
79
 
5.7%
68
 
5.1%
38
 
5.1%
58
 
5.1%
Other values (2)11
 
7.0%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131
17.9%
230
17.3%
,16
9.2%
E16
9.2%
+16
9.2%
410
 
5.8%
910
 
5.8%
79
 
5.2%
68
 
4.6%
38
 
4.6%
Other values (3)19
11.0%

GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,30366E+13
 
1
1,38146E+13
 
1
1,44519E+13
 
1
1,47128E+13
 
1
1,44489E+13
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.8125
Min length10

Characters and Unicode

Total characters173
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,30366E+13
2nd row1,38146E+13
3rd row1,44519E+13
4th row1,47128E+13
5th row1,44489E+13

Common Values

ValueCountFrequency (%)
1,30366E+131
 
6.2%
1,38146E+131
 
6.2%
1,44519E+131
 
6.2%
1,47128E+131
 
6.2%
1,44489E+131
 
6.2%
1,49921E+131
 
6.2%
1,55426E+131
 
6.2%
1,6197E+131
 
6.2%
1,67848E+131
 
6.2%
1,75272E+131
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.009814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,30366e+131
 
6.2%
1,38146e+131
 
6.2%
1,44519e+131
 
6.2%
1,47128e+131
 
6.2%
1,44489e+131
 
6.2%
1,49921e+131
 
6.2%
1,55426e+131
 
6.2%
1,6197e+131
 
6.2%
1,67848e+131
 
6.2%
1,75272e+131
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
138
22.0%
325
14.5%
,16
9.2%
E16
9.2%
+16
9.2%
413
 
7.5%
210
 
5.8%
88
 
4.6%
98
 
4.6%
67
 
4.0%
Other values (3)16
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125
72.3%
Other Punctuation16
 
9.2%
Uppercase Letter16
 
9.2%
Math Symbol16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
138
30.4%
325
20.0%
413
 
10.4%
210
 
8.0%
88
 
6.4%
98
 
6.4%
67
 
5.6%
57
 
5.6%
76
 
4.8%
03
 
2.4%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common157
90.8%
Latin16
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
138
24.2%
325
15.9%
,16
10.2%
+16
10.2%
413
 
8.3%
210
 
6.4%
88
 
5.1%
98
 
5.1%
67
 
4.5%
57
 
4.5%
Other values (2)9
 
5.7%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
22.0%
325
14.5%
,16
9.2%
E16
9.2%
+16
9.2%
413
 
7.5%
210
 
5.8%
88
 
4.6%
98
 
4.6%
67
 
4.0%
Other values (3)16
9.2%

GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,54483E+12
 
1
2,71706E+12
 
1
3,10618E+12
 
1
2,93888E+12
 
1
2,4258E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters174
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,54483E+12
2nd row2,71706E+12
3rd row3,10618E+12
4th row2,93888E+12
5th row2,4258E+12

Common Values

ValueCountFrequency (%)
2,54483E+121
 
6.2%
2,71706E+121
 
6.2%
3,10618E+121
 
6.2%
2,93888E+121
 
6.2%
2,4258E+121
 
6.2%
2,49111E+121
 
6.2%
2,67489E+121
 
6.2%
2,71916E+121
 
6.2%
2,80329E+121
 
6.2%
3,08717E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.100362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,54483e+121
 
6.2%
2,71706e+121
 
6.2%
3,10618e+121
 
6.2%
2,93888e+121
 
6.2%
2,4258e+121
 
6.2%
2,49111e+121
 
6.2%
2,67489e+121
 
6.2%
2,71916e+121
 
6.2%
2,80329e+121
 
6.2%
3,08717e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
235
20.1%
125
14.4%
,16
9.2%
E16
9.2%
+16
9.2%
813
 
7.5%
712
 
6.9%
911
 
6.3%
07
 
4.0%
67
 
4.0%
Other values (3)16
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number126
72.4%
Other Punctuation16
 
9.2%
Uppercase Letter16
 
9.2%
Math Symbol16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
235
27.8%
125
19.8%
813
 
10.3%
712
 
9.5%
911
 
8.7%
07
 
5.6%
67
 
5.6%
56
 
4.8%
45
 
4.0%
35
 
4.0%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common158
90.8%
Latin16
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
235
22.2%
125
15.8%
,16
10.1%
+16
10.1%
813
 
8.2%
712
 
7.6%
911
 
7.0%
07
 
4.4%
67
 
4.4%
56
 
3.8%
Other values (2)10
 
6.3%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
235
20.1%
125
14.4%
,16
9.2%
E16
9.2%
+16
9.2%
813
 
7.5%
712
 
6.9%
911
 
6.3%
07
 
4.0%
67
 
4.0%
Other values (3)16
9.2%

GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
8,91634E+11
 
1
1,10763E+12
 
1
1,39711E+12
 
1
1,69586E+12
 
1
1,667E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length9

Characters and Unicode

Total characters174
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row8,91634E+11
2nd row1,10763E+12
3rd row1,39711E+12
4th row1,69586E+12
5th row1,667E+12

Common Values

ValueCountFrequency (%)
8,91634E+111
 
6.2%
1,10763E+121
 
6.2%
1,39711E+121
 
6.2%
1,69586E+121
 
6.2%
1,667E+121
 
6.2%
2,20884E+121
 
6.2%
2,61616E+121
 
6.2%
2,46523E+121
 
6.2%
2,47282E+121
 
6.2%
2,45604E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.198040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8,91634e+111
 
6.2%
1,10763e+121
 
6.2%
1,39711e+121
 
6.2%
1,69586e+121
 
6.2%
1,667e+121
 
6.2%
2,20884e+121
 
6.2%
2,61616e+121
 
6.2%
2,46523e+121
 
6.2%
2,47282e+121
 
6.2%
2,45604e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
135
20.1%
228
16.1%
,16
9.2%
E16
9.2%
+16
9.2%
614
 
8.0%
49
 
5.2%
88
 
4.6%
78
 
4.6%
97
 
4.0%
Other values (3)17
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number126
72.4%
Other Punctuation16
 
9.2%
Uppercase Letter16
 
9.2%
Math Symbol16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
135
27.8%
228
22.2%
614
 
11.1%
49
 
7.1%
88
 
6.3%
78
 
6.3%
97
 
5.6%
37
 
5.6%
05
 
4.0%
55
 
4.0%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common158
90.8%
Latin16
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
135
22.2%
228
17.7%
,16
10.1%
+16
10.1%
614
 
8.9%
49
 
5.7%
88
 
5.1%
78
 
5.1%
97
 
4.4%
37
 
4.4%
Other values (2)10
 
6.3%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
135
20.1%
228
16.1%
,16
9.2%
E16
9.2%
+16
9.2%
614
 
8.0%
49
 
5.2%
88
 
4.6%
78
 
4.6%
97
 
4.0%
Other values (3)17
9.8%

GDP (current US$) [NY.GDP.MKTP.CD] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
8,20382E+11
 
1
9,4026E+11
 
1
1,21674E+12
 
1
1,1989E+12
 
1
1,34189E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.75
Min length10

Characters and Unicode

Total characters172
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row8,20382E+11
2nd row9,4026E+11
3rd row1,21674E+12
4th row1,1989E+12
5th row1,34189E+12

Common Values

ValueCountFrequency (%)
8,20382E+111
 
6.2%
9,4026E+111
 
6.2%
1,21674E+121
 
6.2%
1,1989E+121
 
6.2%
1,34189E+121
 
6.2%
1,67562E+121
 
6.2%
1,82305E+121
 
6.2%
1,82764E+121
 
6.2%
1,85672E+121
 
6.2%
2,03913E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.292276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8,20382e+111
 
6.2%
9,4026e+111
 
6.2%
1,21674e+121
 
6.2%
1,1989e+121
 
6.2%
1,34189e+121
 
6.2%
1,67562e+121
 
6.2%
1,82305e+121
 
6.2%
1,82764e+121
 
6.2%
1,85672e+121
 
6.2%
2,03913e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
134
19.8%
231
18.0%
,16
9.3%
E16
9.3%
+16
9.3%
89
 
5.2%
69
 
5.2%
08
 
4.7%
97
 
4.1%
77
 
4.1%
Other values (3)19
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number124
72.1%
Other Punctuation16
 
9.3%
Uppercase Letter16
 
9.3%
Math Symbol16
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
134
27.4%
231
25.0%
89
 
7.3%
69
 
7.3%
08
 
6.5%
97
 
5.6%
77
 
5.6%
57
 
5.6%
36
 
4.8%
46
 
4.8%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common156
90.7%
Latin16
 
9.3%

Most frequent character per script

Common
ValueCountFrequency (%)
134
21.8%
231
19.9%
,16
10.3%
+16
10.3%
89
 
5.8%
69
 
5.8%
08
 
5.1%
97
 
4.5%
77
 
4.5%
57
 
4.5%
Other values (2)12
 
7.7%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
19.8%
231
18.0%
,16
9.3%
E16
9.3%
+16
9.3%
89
 
5.2%
69
 
5.2%
08
 
4.7%
97
 
4.1%
77
 
4.1%
Other values (3)19
11.0%

GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
8,77476E+11
 
1
9,75387E+11
 
1
1,0527E+12
 
1
1,10999E+12
 
1
9,00045E+11
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters174
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row8,77476E+11
2nd row9,75387E+11
3rd row1,0527E+12
4th row1,10999E+12
5th row9,00045E+11

Common Values

ValueCountFrequency (%)
8,77476E+111
 
6.2%
9,75387E+111
 
6.2%
1,0527E+121
 
6.2%
1,10999E+121
 
6.2%
9,00045E+111
 
6.2%
1,0578E+121
 
6.2%
1,18049E+121
 
6.2%
1,20109E+121
 
6.2%
1,27444E+121
 
6.2%
1,31535E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.393872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8,77476e+111
 
6.2%
9,75387e+111
 
6.2%
1,0527e+121
 
6.2%
1,10999e+121
 
6.2%
9,00045e+111
 
6.2%
1,0578e+121
 
6.2%
1,18049e+121
 
6.2%
1,20109e+121
 
6.2%
1,27444e+121
 
6.2%
1,31535e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
141
23.6%
221
12.1%
,16
 
9.2%
E16
 
9.2%
+16
 
9.2%
712
 
6.9%
911
 
6.3%
011
 
6.3%
49
 
5.2%
87
 
4.0%
Other values (3)14
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number126
72.4%
Other Punctuation16
 
9.2%
Uppercase Letter16
 
9.2%
Math Symbol16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
141
32.5%
221
16.7%
712
 
9.5%
911
 
8.7%
011
 
8.7%
49
 
7.1%
87
 
5.6%
57
 
5.6%
35
 
4.0%
62
 
1.6%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common158
90.8%
Latin16
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
141
25.9%
221
13.3%
,16
 
10.1%
+16
 
10.1%
712
 
7.6%
911
 
7.0%
011
 
7.0%
49
 
5.7%
87
 
4.4%
57
 
4.4%
Other values (2)7
 
4.4%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141
23.6%
221
12.1%
,16
 
9.2%
E16
 
9.2%
+16
 
9.2%
712
 
6.9%
911
 
6.3%
011
 
6.3%
49
 
5.2%
87
 
4.0%
Other values (3)14
 
8.0%

GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,88868E+11
 
1
3,03861E+11
 
1
3,33075E+11
 
1
3,16132E+11
 
1
3,29753E+11
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,88868E+11
2nd row3,03861E+11
3rd row3,33075E+11
4th row3,16132E+11
5th row3,29753E+11

Common Values

ValueCountFrequency (%)
2,88868E+111
 
6.2%
3,03861E+111
 
6.2%
3,33075E+111
 
6.2%
3,16132E+111
 
6.2%
3,29753E+111
 
6.2%
4,17365E+111
 
6.2%
4,58202E+111
 
6.2%
4,34401E+111
 
6.2%
4,00886E+111
 
6.2%
3,81199E+111
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.497428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,88868e+111
 
6.2%
3,03861e+111
 
6.2%
3,33075e+111
 
6.2%
3,16132e+111
 
6.2%
3,29753e+111
 
6.2%
4,17365e+111
 
6.2%
4,58202e+111
 
6.2%
4,34401e+111
 
6.2%
4,00886e+111
 
6.2%
3,81199e+111
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
141
23.4%
320
11.4%
,16
 
9.1%
E16
 
9.1%
+16
 
9.1%
414
 
8.0%
813
 
7.4%
28
 
4.6%
67
 
4.0%
07
 
4.0%
Other values (3)17
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number127
72.6%
Other Punctuation16
 
9.1%
Uppercase Letter16
 
9.1%
Math Symbol16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
141
32.3%
320
15.7%
414
 
11.0%
813
 
10.2%
28
 
6.3%
67
 
5.5%
07
 
5.5%
57
 
5.5%
75
 
3.9%
95
 
3.9%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common159
90.9%
Latin16
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
141
25.8%
320
12.6%
,16
 
10.1%
+16
 
10.1%
414
 
8.8%
813
 
8.2%
28
 
5.0%
67
 
4.4%
07
 
4.4%
57
 
4.4%
Other values (2)10
 
6.3%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141
23.4%
320
11.4%
,16
 
9.1%
E16
 
9.1%
+16
 
9.1%
414
 
8.0%
813
 
7.4%
28
 
4.6%
67
 
4.0%
07
 
4.0%
Other values (3)17
9.7%

GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,28597E+12
 
1
2,75213E+12
 
1
3,55034E+12
 
1
4,59431E+12
 
1
5,1017E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters174
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,28597E+12
2nd row2,75213E+12
3rd row3,55034E+12
4th row4,59431E+12
5th row5,1017E+12

Common Values

ValueCountFrequency (%)
2,28597E+121
 
6.2%
2,75213E+121
 
6.2%
3,55034E+121
 
6.2%
4,59431E+121
 
6.2%
5,1017E+121
 
6.2%
6,08716E+121
 
6.2%
7,5515E+121
 
6.2%
8,53223E+121
 
6.2%
9,57041E+121
 
6.2%
1,04757E+131
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.594102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,28597e+121
 
6.2%
2,75213e+121
 
6.2%
3,55034e+121
 
6.2%
4,59431e+121
 
6.2%
5,1017e+121
 
6.2%
6,08716e+121
 
6.2%
7,5515e+121
 
6.2%
8,53223e+121
 
6.2%
9,57041e+121
 
6.2%
1,04757e+131
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
134
19.5%
220
11.5%
318
10.3%
,16
9.2%
E16
9.2%
+16
9.2%
512
 
6.9%
711
 
6.3%
49
 
5.2%
07
 
4.0%
Other values (3)15
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number126
72.4%
Other Punctuation16
 
9.2%
Uppercase Letter16
 
9.2%
Math Symbol16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
134
27.0%
220
15.9%
318
14.3%
512
 
9.5%
711
 
8.7%
49
 
7.1%
07
 
5.6%
96
 
4.8%
85
 
4.0%
64
 
3.2%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common158
90.8%
Latin16
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
134
21.5%
220
12.7%
318
11.4%
,16
10.1%
+16
10.1%
512
 
7.6%
711
 
7.0%
49
 
5.7%
07
 
4.4%
96
 
3.8%
Other values (2)9
 
5.7%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
19.5%
220
11.5%
318
10.3%
,16
9.2%
E16
9.2%
+16
9.2%
512
 
6.9%
711
 
6.3%
49
 
5.2%
07
 
4.0%
Other values (3)15
8.6%

GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
4,77779E+13
 
1
5,17787E+13
 
1
5,83375E+13
 
1
6,40715E+13
 
1
6,0781E+13
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters174
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row4,77779E+13
2nd row5,17787E+13
3rd row5,83375E+13
4th row6,40715E+13
5th row6,0781E+13

Common Values

ValueCountFrequency (%)
4,77779E+131
 
6.2%
5,17787E+131
 
6.2%
5,83375E+131
 
6.2%
6,40715E+131
 
6.2%
6,0781E+131
 
6.2%
6,65005E+131
 
6.2%
7,36715E+131
 
6.2%
7,53116E+131
 
6.2%
7,74432E+131
 
6.2%
7,95755E+131
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.694773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,77779e+131
 
6.2%
5,17787e+131
 
6.2%
5,83375e+131
 
6.2%
6,40715e+131
 
6.2%
6,0781e+131
 
6.2%
6,65005e+131
 
6.2%
7,36715e+131
 
6.2%
7,53116e+131
 
6.2%
7,74432e+131
 
6.2%
7,95755e+131
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
128
16.1%
724
13.8%
324
13.8%
,16
9.2%
E16
9.2%
+16
9.2%
513
7.5%
610
 
5.7%
48
 
4.6%
88
 
4.6%
Other values (3)11
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number126
72.4%
Other Punctuation16
 
9.2%
Uppercase Letter16
 
9.2%
Math Symbol16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
128
22.2%
724
19.0%
324
19.0%
513
10.3%
610
 
7.9%
48
 
6.3%
88
 
6.3%
25
 
4.0%
04
 
3.2%
92
 
1.6%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common158
90.8%
Latin16
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
128
17.7%
724
15.2%
324
15.2%
,16
10.1%
+16
10.1%
513
8.2%
610
 
6.3%
48
 
5.1%
88
 
5.1%
25
 
3.2%
Other values (2)6
 
3.8%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
128
16.1%
724
13.8%
324
13.8%
,16
9.2%
E16
9.2%
+16
9.2%
513
7.5%
610
 
5.7%
48
 
4.6%
88
 
4.6%
Other values (3)11
 
6.3%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,065629701
 
1
1,199302958
 
1
1,172471765
 
1
1,209034004
 
1
1,310604073
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters176
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,065629701
2nd row1,199302958
3rd row1,172471765
4th row1,209034004
5th row1,310604073

Common Values

ValueCountFrequency (%)
1,0656297011
 
6.2%
1,1993029581
 
6.2%
1,1724717651
 
6.2%
1,2090340041
 
6.2%
1,3106040731
 
6.2%
1,2668266261
 
6.2%
1,2061503361
 
6.2%
1,2416774791
 
6.2%
1,1852579011
 
6.2%
1,1499419971
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.791945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,0656297011
 
6.2%
1,1993029581
 
6.2%
1,1724717651
 
6.2%
1,2090340041
 
6.2%
1,3106040731
 
6.2%
1,2668266261
 
6.2%
1,2061503361
 
6.2%
1,2416774791
 
6.2%
1,1852579011
 
6.2%
1,1499419971
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
134
19.3%
618
10.2%
917
9.7%
717
9.7%
,16
9.1%
016
9.1%
416
9.1%
213
 
7.4%
312
 
6.8%
59
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
134
21.2%
618
11.2%
917
10.6%
717
10.6%
016
10.0%
416
10.0%
213
 
8.1%
312
 
7.5%
59
 
5.6%
88
 
5.0%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
134
19.3%
618
10.2%
917
9.7%
717
9.7%
,16
9.1%
016
9.1%
416
9.1%
213
 
7.4%
312
 
6.8%
59
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
19.3%
618
10.2%
917
9.7%
717
9.7%
,16
9.1%
016
9.1%
416
9.1%
213
 
7.4%
312
 
6.8%
59
 
5.1%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,023706919
 
1
1,9750983
 
1
1,907528634
 
1
1,897075896
 
1
2,098147335
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.75
Min length9

Characters and Unicode

Total characters172
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,023706919
2nd row1,9750983
3rd row1,907528634
4th row1,897075896
5th row2,098147335

Common Values

ValueCountFrequency (%)
2,0237069191
 
6.2%
1,97509831
 
6.2%
1,9075286341
 
6.2%
1,8970758961
 
6.2%
2,0981473351
 
6.2%
1,9694193531
 
6.2%
1,8914062141
 
6.2%
1,87108061
 
6.2%
1,8498759181
 
6.2%
1,8629614391
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:31.892667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,0237069191
 
6.2%
1,97509831
 
6.2%
1,9075286341
 
6.2%
1,8970758961
 
6.2%
2,0981473351
 
6.2%
1,9694193531
 
6.2%
1,8914062141
 
6.2%
1,87108061
 
6.2%
1,8498759181
 
6.2%
1,8629614391
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
127
15.7%
922
12.8%
820
11.6%
216
9.3%
,16
9.3%
013
7.6%
313
7.6%
712
7.0%
412
7.0%
611
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number156
90.7%
Other Punctuation16
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
127
17.3%
922
14.1%
820
12.8%
216
10.3%
013
8.3%
313
8.3%
712
7.7%
412
7.7%
611
7.1%
510
 
6.4%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common172
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
127
15.7%
922
12.8%
820
11.6%
216
9.3%
,16
9.3%
013
7.6%
313
7.6%
712
7.0%
412
7.0%
611
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127
15.7%
922
12.8%
820
11.6%
216
9.3%
,16
9.3%
013
7.6%
313
7.6%
712
7.0%
412
7.0%
611
6.4%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,605170416
 
1
1,525482905
 
1
1,447052519
 
1
1,535674984
 
1
1,554210702
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters176
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,605170416
2nd row1,525482905
3rd row1,447052519
4th row1,535674984
5th row1,554210702

Common Values

ValueCountFrequency (%)
1,6051704161
 
6.2%
1,5254829051
 
6.2%
1,4470525191
 
6.2%
1,5356749841
 
6.2%
1,5542107021
 
6.2%
1,5004945761
 
6.2%
1,4759565971
 
6.2%
1,4269249771
 
6.2%
1,3990204191
 
6.2%
1,2829696541
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.000164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,6051704161
 
6.2%
1,5254829051
 
6.2%
1,4470525191
 
6.2%
1,5356749841
 
6.2%
1,5542107021
 
6.2%
1,5004945761
 
6.2%
1,4759565971
 
6.2%
1,4269249771
 
6.2%
1,3990204191
 
6.2%
1,2829696541
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
527
15.3%
124
13.6%
218
10.2%
417
9.7%
917
9.7%
,16
9.1%
714
8.0%
613
7.4%
012
6.8%
310
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
527
16.9%
124
15.0%
218
11.2%
417
10.6%
917
10.6%
714
8.8%
613
8.1%
012
7.5%
310
 
6.2%
88
 
5.0%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
527
15.3%
124
13.6%
218
10.2%
417
9.7%
917
9.7%
,16
9.1%
714
8.0%
613
7.4%
012
6.8%
310
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
527
15.3%
124
13.6%
218
10.2%
417
9.7%
917
9.7%
,16
9.1%
714
8.0%
613
7.4%
012
6.8%
310
 
5.7%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
0,931581653
 
1
0,917197038
 
1
0,897626654
 
1
0,920247843
 
1
0,983777357
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters174
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row0,931581653
2nd row0,917197038
3rd row0,897626654
4th row0,920247843
5th row0,983777357

Common Values

ValueCountFrequency (%)
0,9315816531
 
6.2%
0,9171970381
 
6.2%
0,8976266541
 
6.2%
0,9202478431
 
6.2%
0,9837773571
 
6.2%
0,9588511331
 
6.2%
0,9868064931
 
6.2%
0,9674272781
 
6.2%
0,950866051
 
6.2%
0,9669993341
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.098520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,9315816531
 
6.2%
0,9171970381
 
6.2%
0,8976266541
 
6.2%
0,9202478431
 
6.2%
0,9837773571
 
6.2%
0,9588511331
 
6.2%
0,9868064931
 
6.2%
0,9674272781
 
6.2%
0,950866051
 
6.2%
0,9669993341
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
928
16.1%
024
13.8%
819
10.9%
318
10.3%
,16
9.2%
614
8.0%
713
7.5%
512
6.9%
211
 
6.3%
410
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
928
17.7%
024
15.2%
819
12.0%
318
11.4%
614
8.9%
713
8.2%
512
7.6%
211
 
7.0%
410
 
6.3%
19
 
5.7%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
928
16.1%
024
13.8%
819
10.9%
318
10.3%
,16
9.2%
614
8.0%
713
7.5%
512
6.9%
211
 
6.3%
410
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
928
16.1%
024
13.8%
819
10.9%
318
10.3%
,16
9.2%
614
8.0%
713
7.5%
512
6.9%
211
 
6.3%
410
 
5.7%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,110669655
 
1
1,125832408
 
1
1,188901783
 
1
1,248621382
 
1
1,377555631
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.8125
Min length9

Characters and Unicode

Total characters173
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,110669655
2nd row1,125832408
3rd row1,188901783
4th row1,248621382
5th row1,377555631

Common Values

ValueCountFrequency (%)
1,1106696551
 
6.2%
1,1258324081
 
6.2%
1,1889017831
 
6.2%
1,2486213821
 
6.2%
1,3775556311
 
6.2%
1,1943383381
 
6.2%
1,1932918951
 
6.2%
1,1184045981
 
6.2%
1,00236721
 
6.2%
0,9899252991
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.205048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,1106696551
 
6.2%
1,1258324081
 
6.2%
1,1889017831
 
6.2%
1,2486213821
 
6.2%
1,3775556311
 
6.2%
1,1943383381
 
6.2%
1,1932918951
 
6.2%
1,1184045981
 
6.2%
1,00236721
 
6.2%
0,9899252991
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
136
20.8%
217
9.8%
,16
9.2%
816
9.2%
915
8.7%
515
8.7%
315
8.7%
413
 
7.5%
011
 
6.4%
611
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number157
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
136
22.9%
217
10.8%
816
10.2%
915
9.6%
515
9.6%
315
9.6%
413
 
8.3%
011
 
7.0%
611
 
7.0%
78
 
5.1%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
136
20.8%
217
9.8%
,16
9.2%
816
9.2%
915
8.7%
515
8.7%
315
8.7%
413
 
7.5%
011
 
6.4%
611
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
136
20.8%
217
9.8%
,16
9.2%
816
9.2%
915
8.7%
515
8.7%
315
8.7%
413
 
7.5%
011
 
6.4%
611
 
6.4%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
3,330103192
 
1
3,245254379
 
1
3,118542079
 
1
3,149486017
 
1
3,924063399
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters176
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row3,330103192
2nd row3,245254379
3rd row3,118542079
4th row3,149486017
5th row3,924063399

Common Values

ValueCountFrequency (%)
3,3301031921
 
6.2%
3,2452543791
 
6.2%
3,1185420791
 
6.2%
3,1494860171
 
6.2%
3,9240633991
 
6.2%
3,5850857211
 
6.2%
3,4330438381
 
6.2%
3,6892404351
 
6.2%
3,8540425831
 
6.2%
4,1129929781
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.305592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,3301031921
 
6.2%
3,2452543791
 
6.2%
3,1185420791
 
6.2%
3,1494860171
 
6.2%
3,9240633991
 
6.2%
3,5850857211
 
6.2%
3,4330438381
 
6.2%
3,6892404351
 
6.2%
3,8540425831
 
6.2%
4,1129929781
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
326
14.8%
423
13.1%
517
9.7%
,16
9.1%
116
9.1%
916
9.1%
216
9.1%
816
9.1%
011
6.2%
711
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
326
16.2%
423
14.4%
517
10.6%
116
10.0%
916
10.0%
216
10.0%
816
10.0%
011
6.9%
711
6.9%
68
 
5.0%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
326
14.8%
423
13.1%
517
9.7%
,16
9.1%
116
9.1%
916
9.1%
216
9.1%
816
9.1%
011
6.2%
711
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
326
14.8%
423
13.1%
517
9.7%
,16
9.1%
116
9.1%
916
9.1%
216
9.1%
816
9.1%
011
6.2%
711
6.2%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
4,090034876
 
1
4,041627237
 
1
4,079655081
 
1
4,463827356
 
1
4,88559968
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters174
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row4,090034876
2nd row4,041627237
3rd row4,079655081
4th row4,463827356
5th row4,88559968

Common Values

ValueCountFrequency (%)
4,0900348761
 
6.2%
4,0416272371
 
6.2%
4,0796550811
 
6.2%
4,4638273561
 
6.2%
4,885599681
 
6.2%
4,9226416771
 
6.2%
4,8401739951
 
6.2%
4,4774012191
 
6.2%
4,0466788791
 
6.2%
3,695894651
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.406805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,0900348761
 
6.2%
4,0416272371
 
6.2%
4,0796550811
 
6.2%
4,4638273561
 
6.2%
4,885599681
 
6.2%
4,9226416771
 
6.2%
4,8401739951
 
6.2%
4,4774012191
 
6.2%
4,0466788791
 
6.2%
3,695894651
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
426
14.9%
318
10.3%
818
10.3%
717
9.8%
,16
9.2%
616
9.2%
116
9.2%
014
8.0%
914
8.0%
210
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
426
16.5%
318
11.4%
818
11.4%
717
10.8%
616
10.1%
116
10.1%
014
8.9%
914
8.9%
210
 
6.3%
59
 
5.7%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
426
14.9%
318
10.3%
818
10.3%
717
9.8%
,16
9.2%
616
9.2%
116
9.2%
014
8.0%
914
8.0%
210
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
426
14.9%
318
10.3%
818
10.3%
717
9.8%
,16
9.2%
616
9.2%
116
9.2%
014
8.0%
914
8.0%
210
 
5.7%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,434203228
 
1
2,373086999
 
1
2,374207748
 
1
2,495723016
 
1
2,653485016
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters176
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,434203228
2nd row2,373086999
3rd row2,374207748
4th row2,495723016
5th row2,653485016

Common Values

ValueCountFrequency (%)
2,4342032281
 
6.2%
2,3730869991
 
6.2%
2,3742077481
 
6.2%
2,4957230161
 
6.2%
2,6534850161
 
6.2%
2,5781595021
 
6.2%
2,5027264021
 
6.2%
2,4205647311
 
6.2%
2,2936402411
 
6.2%
2,1839062481
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.522967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,4342032281
 
6.2%
2,3730869991
 
6.2%
2,3742077481
 
6.2%
2,4957230161
 
6.2%
2,6534850161
 
6.2%
2,5781595021
 
6.2%
2,5027264021
 
6.2%
2,4205647311
 
6.2%
2,2936402411
 
6.2%
2,1839062481
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
230
17.0%
420
11.4%
017
9.7%
,16
9.1%
516
9.1%
315
8.5%
914
8.0%
613
7.4%
712
 
6.8%
112
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
230
18.8%
420
12.5%
017
10.6%
516
10.0%
315
9.4%
914
8.8%
613
8.1%
712
 
7.5%
112
 
7.5%
811
 
6.9%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
230
17.0%
420
11.4%
017
9.7%
,16
9.1%
516
9.1%
315
8.5%
914
8.0%
613
7.4%
712
 
6.8%
112
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
230
17.0%
420
11.4%
017
9.7%
,16
9.1%
516
9.1%
315
8.5%
914
8.0%
613
7.4%
712
 
6.8%
112
 
6.8%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,524013393
 
1
1,481084895
 
1
1,4662921
 
1
1,441924093
 
1
1,538625697
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length9

Characters and Unicode

Total characters174
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,524013393
2nd row1,481084895
3rd row1,4662921
4th row1,441924093
5th row1,538625697

Common Values

ValueCountFrequency (%)
1,5240133931
 
6.2%
1,4810848951
 
6.2%
1,46629211
 
6.2%
1,4419240931
 
6.2%
1,5386256971
 
6.2%
1,5394069811
 
6.2%
1,4118511591
 
6.2%
1,3786564651
 
6.2%
1,3294460841
 
6.2%
1,3302444231
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.628251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,5240133931
 
6.2%
1,4810848951
 
6.2%
1,46629211
 
6.2%
1,4419240931
 
6.2%
1,5386256971
 
6.2%
1,5394069811
 
6.2%
1,4118511591
 
6.2%
1,3786564651
 
6.2%
1,3294460841
 
6.2%
1,3302444231
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
133
19.0%
426
14.9%
318
10.3%
,16
9.2%
516
9.2%
914
8.0%
614
8.0%
812
 
6.9%
211
 
6.3%
08
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
133
20.9%
426
16.5%
318
11.4%
516
10.1%
914
8.9%
614
8.9%
812
 
7.6%
211
 
7.0%
08
 
5.1%
76
 
3.8%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
133
19.0%
426
14.9%
318
10.3%
,16
9.2%
516
9.2%
914
8.0%
614
8.0%
812
 
6.9%
211
 
6.3%
08
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133
19.0%
426
14.9%
318
10.3%
,16
9.2%
516
9.2%
914
8.0%
614
8.0%
812
 
6.9%
211
 
6.3%
08
 
4.6%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,754907666
 
1
2,52680863
 
1
2,478248209
 
1
2,631462141
 
1
3,129387179
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.8125
Min length9

Characters and Unicode

Total characters173
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,754907666
2nd row2,52680863
3rd row2,478248209
4th row2,631462141
5th row3,129387179

Common Values

ValueCountFrequency (%)
2,7549076661
 
6.2%
2,526808631
 
6.2%
2,4782482091
 
6.2%
2,6314621411
 
6.2%
3,1293871791
 
6.2%
2,8894613281
 
6.2%
2,7044835581
 
6.2%
2,6181676191
 
6.2%
2,5488256791
 
6.2%
2,5439825031
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.756005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,7549076661
 
6.2%
2,526808631
 
6.2%
2,4782482091
 
6.2%
2,6314621411
 
6.2%
3,1293871791
 
6.2%
2,8894613281
 
6.2%
2,7044835581
 
6.2%
2,6181676191
 
6.2%
2,5488256791
 
6.2%
2,5439825031
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
228
16.2%
820
11.6%
417
9.8%
617
9.8%
,16
9.2%
116
9.2%
515
8.7%
712
6.9%
312
6.9%
910
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number157
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
228
17.8%
820
12.7%
417
10.8%
617
10.8%
116
10.2%
515
9.6%
712
7.6%
312
7.6%
910
 
6.4%
010
 
6.4%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
228
16.2%
820
11.6%
417
9.8%
617
9.8%
,16
9.2%
116
9.2%
515
8.7%
712
6.9%
312
6.9%
910
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
228
16.2%
820
11.6%
417
9.8%
617
9.8%
,16
9.2%
116
9.2%
515
8.7%
712
6.9%
312
6.9%
910
 
5.8%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
0,355958931
 
1
0,311171936
 
1
0,401163918
 
1
0,390513227
 
1
0,501556275
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row0,355958931
2nd row0,311171936
3rd row0,401163918
4th row0,390513227
5th row0,501556275

Common Values

ValueCountFrequency (%)
0,3559589311
 
6.2%
0,3111719361
 
6.2%
0,4011639181
 
6.2%
0,3905132271
 
6.2%
0,5015562751
 
6.2%
0,4527344931
 
6.2%
0,4657778031
 
6.2%
0,4759872811
 
6.2%
0,5079194551
 
6.2%
0,5138299571
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.863661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,3559589311
 
6.2%
0,3111719361
 
6.2%
0,4011639181
 
6.2%
0,3905132271
 
6.2%
0,5015562751
 
6.2%
0,4527344931
 
6.2%
0,4657778031
 
6.2%
0,4759872811
 
6.2%
0,5079194551
 
6.2%
0,5138299571
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
024
13.7%
523
13.1%
118
10.3%
717
9.7%
417
9.7%
,16
9.1%
915
8.6%
314
8.0%
613
7.4%
211
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024
15.1%
523
14.5%
118
11.3%
717
10.7%
417
10.7%
915
9.4%
314
8.8%
613
8.2%
211
6.9%
87
 
4.4%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024
13.7%
523
13.1%
118
10.3%
717
9.7%
417
9.7%
,16
9.1%
915
8.6%
314
8.0%
613
7.4%
211
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024
13.7%
523
13.1%
118
10.3%
717
9.7%
417
9.7%
,16
9.1%
915
8.6%
314
8.0%
613
7.4%
211
6.3%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,38376969
 
1
1,290746983
 
1
1,177517253
 
1
1,145843314
 
1
1,214002043
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,38376969
2nd row1,290746983
3rd row1,177517253
4th row1,145843314
5th row1,214002043

Common Values

ValueCountFrequency (%)
1,383769691
 
6.2%
1,2907469831
 
6.2%
1,1775172531
 
6.2%
1,1458433141
 
6.2%
1,2140020431
 
6.2%
1,1158082511
 
6.2%
1,1032576761
 
6.2%
1,1327931121
 
6.2%
1,1232742621
 
6.2%
1,1094378831
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:32.965562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,383769691
 
6.2%
1,2907469831
 
6.2%
1,1775172531
 
6.2%
1,1458433141
 
6.2%
1,2140020431
 
6.2%
1,1158082511
 
6.2%
1,1032576761
 
6.2%
1,1327931121
 
6.2%
1,1232742621
 
6.2%
1,1094378831
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
132
18.3%
318
10.3%
018
10.3%
,16
9.1%
916
9.1%
715
8.6%
214
8.0%
413
7.4%
512
 
6.9%
811
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
132
20.1%
318
11.3%
018
11.3%
916
10.1%
715
9.4%
214
8.8%
413
8.2%
512
 
7.5%
811
 
6.9%
610
 
6.3%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
132
18.3%
318
10.3%
018
10.3%
,16
9.1%
916
9.1%
715
8.6%
214
8.0%
413
7.4%
512
 
6.9%
811
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
132
18.3%
318
10.3%
018
10.3%
,16
9.1%
916
9.1%
715
8.6%
214
8.0%
413
7.4%
512
 
6.9%
811
 
6.3%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,85334244
 
1
1,85464884
 
1
1,739812859
 
1
1,712334361
 
1
1,886133152
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.75
Min length10

Characters and Unicode

Total characters172
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,85334244
2nd row1,85464884
3rd row1,739812859
4th row1,712334361
5th row1,886133152

Common Values

ValueCountFrequency (%)
1,853342441
 
6.2%
1,854648841
 
6.2%
1,7398128591
 
6.2%
1,7123343611
 
6.2%
1,8861331521
 
6.2%
1,739475151
 
6.2%
1,6655760531
 
6.2%
1,6933681631
 
6.2%
1,7028550961
 
6.2%
1,7286890681
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:33.058364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,853342441
 
6.2%
1,854648841
 
6.2%
1,7398128591
 
6.2%
1,7123343611
 
6.2%
1,8861331521
 
6.2%
1,739475151
 
6.2%
1,6655760531
 
6.2%
1,6933681631
 
6.2%
1,7028550961
 
6.2%
1,7286890681
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
125
14.5%
725
14.5%
319
11.0%
517
9.9%
617
9.9%
,16
9.3%
816
9.3%
411
6.4%
911
6.4%
28
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number156
90.7%
Other Punctuation16
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
125
16.0%
725
16.0%
319
12.2%
517
10.9%
617
10.9%
816
10.3%
411
7.1%
911
7.1%
28
 
5.1%
07
 
4.5%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common172
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
125
14.5%
725
14.5%
319
11.0%
517
9.9%
617
9.9%
,16
9.3%
816
9.3%
411
6.4%
911
6.4%
28
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
125
14.5%
725
14.5%
319
11.0%
517
9.9%
617
9.9%
,16
9.3%
816
9.3%
411
6.4%
911
6.4%
28
 
4.7%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
2,394431087
 
1
2,364165577
 
1
2,328462871
 
1
2,386424706
 
1
2,617904287
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.8125
Min length10

Characters and Unicode

Total characters173
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row2,394431087
2nd row2,364165577
3rd row2,328462871
4th row2,386424706
5th row2,617904287

Common Values

ValueCountFrequency (%)
2,3944310871
 
6.2%
2,3641655771
 
6.2%
2,3284628711
 
6.2%
2,3864247061
 
6.2%
2,6179042871
 
6.2%
2,5229104951
 
6.2%
2,4120333841
 
6.2%
2,372881791
 
6.2%
2,304419431
 
6.2%
2,2510887511
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:33.152352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,3944310871
 
6.2%
2,3641655771
 
6.2%
2,3284628711
 
6.2%
2,3864247061
 
6.2%
2,6179042871
 
6.2%
2,5229104951
 
6.2%
2,4120333841
 
6.2%
2,372881791
 
6.2%
2,304419431
 
6.2%
2,2510887511
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
233
19.1%
117
9.8%
817
9.8%
,16
9.2%
316
9.2%
616
9.2%
415
8.7%
714
8.1%
511
 
6.4%
010
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number157
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
233
21.0%
117
10.8%
817
10.8%
316
10.2%
616
10.2%
415
9.6%
714
8.9%
511
 
7.0%
010
 
6.4%
98
 
5.1%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
233
19.1%
117
9.8%
817
9.8%
,16
9.2%
316
9.2%
616
9.2%
415
8.7%
714
8.1%
511
 
6.4%
010
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
233
19.1%
117
9.8%
817
9.8%
,16
9.2%
316
9.2%
616
9.2%
415
8.7%
714
8.1%
511
 
6.4%
010
 
5.8%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.28294262 × 1010
Minimum3.032496518 × 1010
Maximum5.27647612 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:33.251467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.032496518 × 1010
5-th percentile3.449398477 × 1010
Q14.004690628 × 1010
median4.402043633 × 1010
Q34.511502045 × 1010
95-th percentile4.994682454 × 1010
Maximum5.27647612 × 1010
Range2.243979602 × 1010
Interquartile range (IQR)5068114169

Descriptive statistics

Standard deviation5244369271
Coefficient of variation (CV)0.122447806
Kurtosis1.405535821
Mean4.28294262 × 1010
Median Absolute Deviation (MAD)2106381406
Skewness-0.6000404362
Sum6.852708192 × 1011
Variance2.750340905 × 1019
MonotonicityNot monotonic
2022-06-22T15:25:33.348324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3.032496518 × 10101
 
6.2%
3.588365797 × 10101
 
6.2%
4.011085928 × 10101
 
6.2%
4.509895631 × 10101
 
6.2%
4.452892784 × 10101
 
6.2%
4.302591501 × 10101
 
6.2%
4.516321288 × 10101
 
6.2%
4.379822535 × 10101
 
6.2%
4.42426473 × 10101
 
6.2%
4.466283117 × 10101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
3.032496518 × 10101
6.2%
3.588365797 × 10101
6.2%
3.817002107 × 10101
6.2%
3.985504731 × 10101
6.2%
4.011085928 × 10101
6.2%
4.221025809 × 10101
6.2%
4.302591501 × 10101
6.2%
4.379822535 × 10101
6.2%
4.42426473 × 10101
6.2%
4.452892784 × 10101
6.2%
ValueCountFrequency (%)
5.27647612 × 10101
6.2%
4.900751232 × 10101
6.2%
4.64230209 × 10101
6.2%
4.516321288 × 10101
6.2%
4.509895631 × 10101
6.2%
4.466283117 × 10101
6.2%
4.452892784 × 10101
6.2%
4.42426473 × 10101
6.2%
4.379822535 × 10101
6.2%
4.302591501 × 10101
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.067082939 × 1010
Minimum4.444205052 × 1010
Maximum5.64414554 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:33.435530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.444205052 × 1010
5-th percentile4.534611636 × 1010
Q14.873939408 × 1010
median5.104714011 × 1010
Q35.284398637 × 1010
95-th percentile5.563483823 × 1010
Maximum5.64414554 × 1010
Range1.199940488 × 1010
Interquartile range (IQR)4104592292

Descriptive statistics

Standard deviation3495173361
Coefficient of variation (CV)0.06897801759
Kurtosis-0.6170381912
Mean5.067082939 × 1010
Median Absolute Deviation (MAD)1969544324
Skewness-0.2818883778
Sum8.107332702 × 1011
Variance1.221623682 × 1019
MonotonicityNot monotonic
2022-06-22T15:25:33.536024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4.444205052 × 10101
 
6.2%
4.579214842 × 10101
 
6.2%
5.068446738 × 10101
 
6.2%
5.536596584 × 10101
 
6.2%
5.64414554 × 10101
 
6.2%
5.204406056 × 10101
 
6.2%
5.412087101 × 10101
 
6.2%
5.02165074 × 10101
 
6.2%
5.200146245 × 10101
 
6.2%
5.31347509 × 10101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
4.444205052 × 10101
6.2%
4.564747164 × 10101
6.2%
4.579214842 × 10101
6.2%
4.737058955 × 10101
6.2%
4.919566225 × 10101
6.2%
5.011892921 × 10101
6.2%
5.02165074 × 10101
6.2%
5.068446738 × 10101
6.2%
5.140981284 × 10101
6.2%
5.200146245 × 10101
6.2%
ValueCountFrequency (%)
5.64414554 × 10101
6.2%
5.536596584 × 10101
6.2%
5.412087101 × 10101
6.2%
5.31347509 × 10101
6.2%
5.274706486 × 10101
6.2%
5.204406056 × 10101
6.2%
5.200146245 × 10101
6.2%
5.140981284 × 10101
6.2%
5.068446738 × 10101
6.2%
5.02165074 × 10101
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.955753512 × 1010
Minimum2.218084507 × 1010
Maximum3.683998975 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:33.626260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.218084507 × 1010
5-th percentile2.431998219 × 1010
Q12.738774898 × 1010
median2.968533233 × 1010
Q33.199202883 × 1010
95-th percentile3.475085843 × 1010
Maximum3.683998975 × 1010
Range1.465914468 × 1010
Interquartile range (IQR)4604279852

Descriptive statistics

Standard deviation3683209286
Coefficient of variation (CV)0.1246115169
Kurtosis0.1972680176
Mean2.955753512 × 1010
Median Absolute Deviation (MAD)2316293544
Skewness0.03061951999
Sum4.729205619 × 1011
Variance1.356603064 × 1019
MonotonicityNot monotonic
2022-06-22T15:25:33.723488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.973764239 × 10101
 
6.2%
2.963302226 × 10101
 
6.2%
3.198243179 × 10101
 
6.2%
3.683998975 × 10101
 
6.2%
3.405448132 × 10101
 
6.2%
3.202081995 × 10101
 
6.2%
3.382880497 × 10101
 
6.2%
2.97810082 × 10101
 
6.2%
2.99574459 × 10101
 
6.2%
2.770103434 × 10101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.218084507 × 10101
6.2%
2.50330279 × 10101
6.2%
2.638067398 × 10101
6.2%
2.644789292 × 10101
6.2%
2.770103434 × 10101
6.2%
2.842009844 × 10101
6.2%
2.892134276 × 10101
6.2%
2.963302226 × 10101
6.2%
2.973764239 × 10101
6.2%
2.97810082 × 10101
6.2%
ValueCountFrequency (%)
3.683998975 × 10101
6.2%
3.405448132 × 10101
6.2%
3.382880497 × 10101
6.2%
3.202081995 × 10101
6.2%
3.198243179 × 10101
6.2%
2.99574459 × 10101
6.2%
2.97810082 × 10101
6.2%
2.973764239 × 10101
6.2%
2.963302226 × 10101
6.2%
2.892134276 × 10101
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.830665168 × 1010
Minimum4.053004569 × 1010
Maximum6.076221384 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:33.817555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.053004569 × 1010
5-th percentile4.129695609 × 1010
Q14.511542718 × 1010
median4.676071074 × 1010
Q34.97277073 × 1010
95-th percentile6.019920111 × 1010
Maximum6.076221384 × 1010
Range2.023216815 × 1010
Interquartile range (IQR)4612280120

Descriptive statistics

Standard deviation5917340439
Coefficient of variation (CV)0.1224953549
Kurtosis0.5290113744
Mean4.830665168 × 1010
Median Absolute Deviation (MAD)2423971836
Skewness0.991726593
Sum7.729064269 × 1011
Variance3.501491787 × 1019
MonotonicityNot monotonic
2022-06-22T15:25:33.918841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4.430061333 × 10101
 
6.2%
4.155259289 × 10101
 
6.2%
4.053004569 × 10101
 
6.2%
4.636146828 × 10101
 
6.2%
5.146515821 × 10101
 
6.2%
5.465545074 × 10101
 
6.2%
6.076221384 × 10101
 
6.2%
6.00115302 × 10101
 
6.2%
4.902393241 × 10101
 
6.2%
4.690346661 × 10101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
4.053004569 × 10101
6.2%
4.155259289 × 10101
6.2%
4.210610331 × 10101
6.2%
4.430061333 × 10101
6.2%
4.53870318 × 10101
6.2%
4.636146828 × 10101
6.2%
4.647128771 × 10101
6.2%
4.661795486 × 10101
6.2%
4.690346661 × 10101
6.2%
4.760901999 × 10101
6.2%
ValueCountFrequency (%)
6.076221384 × 10101
6.2%
6.00115302 × 10101
6.2%
5.465545074 × 10101
6.2%
5.146515821 × 10101
6.2%
4.9148557 × 10101
6.2%
4.902393241 × 10101
6.2%
4.760901999 × 10101
6.2%
4.690346661 × 10101
6.2%
4.661795486 × 10101
6.2%
4.647128771 × 10101
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.916893972 × 1010
Minimum1.298813296 × 1010
Maximum2.275484713 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:34.014320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.298813296 × 1010
5-th percentile1.435445284 × 1010
Q11.783592424 × 1010
median1.912595744 × 1010
Q32.159639276 × 1010
95-th percentile2.273570747 × 1010
Maximum2.275484713 × 1010
Range9766714165
Interquartile range (IQR)3760468514

Descriptive statistics

Standard deviation2792040948
Coefficient of variation (CV)0.1456544279
Kurtosis0.1763126567
Mean1.916893972 × 1010
Median Absolute Deviation (MAD)1525999702
Skewness-0.5925161751
Sum3.067030356 × 1011
Variance7.795492656 × 1018
MonotonicityNot monotonic
2022-06-22T15:25:34.110452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.298813296 × 10101
 
6.2%
1.48098928 × 10101
 
6.2%
1.741713993 × 10101
 
6.2%
1.93420584 × 10101
 
6.2%
1.893622605 × 10101
 
6.2%
1.931568882 × 10101
 
6.2%
2.139372086 × 10101
 
6.2%
2.045210711 × 10101
 
6.2%
1.851573121 × 10101
 
6.2%
1.785364048 × 10101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
1.298813296 × 10101
6.2%
1.48098928 × 10101
6.2%
1.741713993 × 10101
6.2%
1.778277554 × 10101
6.2%
1.785364048 × 10101
6.2%
1.79376419 × 10101
6.2%
1.851573121 × 10101
6.2%
1.893622605 × 10101
6.2%
1.931568882 × 10101
6.2%
1.93420584 × 10101
6.2%
ValueCountFrequency (%)
2.275484713 × 10101
6.2%
2.272932758 × 10101
6.2%
2.226969632 × 10101
6.2%
2.220440844 × 10101
6.2%
2.139372086 × 10101
6.2%
2.045210711 × 10101
6.2%
1.93420584 × 10101
6.2%
1.931568882 × 10101
6.2%
1.893622605 × 10101
6.2%
1.851573121 × 10101
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.17303398 × 1010
Minimum2.733697727 × 1010
Maximum8.835289646 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:34.199261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.733697727 × 1010
5-th percentile3.272258053 × 1010
Q15.502086824 × 1010
median6.345693651 × 1010
Q36.94933519 × 1010
95-th percentile8.561060261 × 1010
Maximum8.835289646 × 1010
Range6.101591919 × 1010
Interquartile range (IQR)1.447248366 × 1010

Descriptive statistics

Standard deviation1.676095 × 1010
Coefficient of variation (CV)0.2715188359
Kurtosis0.09269920438
Mean6.17303398 × 1010
Median Absolute Deviation (MAD)7026869279
Skewness-0.4550148613
Sum9.876854367 × 1011
Variance2.809294449 × 1020
MonotonicityNot monotonic
2022-06-22T15:25:34.295663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.733697727 × 10101
 
6.2%
3.451778162 × 10101
 
6.2%
4.3534995 × 10101
 
6.2%
5.618378539 × 10101
 
6.2%
5.15321168 × 10101
 
6.2%
5.872022761 × 10101
 
6.2%
7.023752395 × 10101
 
6.2%
8.146939993 × 10101
 
6.2%
8.835289646 × 10101
 
6.2%
8.469650465 × 10101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.733697727 × 10101
6.2%
3.451778162 × 10101
6.2%
4.3534995 × 10101
6.2%
5.15321168 × 10101
6.2%
5.618378539 × 10101
6.2%
5.872022761 × 10101
6.2%
6.160920476 × 10101
6.2%
6.171253717 × 10101
6.2%
6.520133585 × 10101
6.2%
6.642182218 × 10101
6.2%
ValueCountFrequency (%)
8.835289646 × 10101
6.2%
8.469650465 × 10101
6.2%
8.146939993 × 10101
6.2%
7.023752395 × 10101
6.2%
6.924529455 × 10101
6.2%
6.691303354 × 10101
6.2%
6.642182218 × 10101
6.2%
6.520133585 × 10101
6.2%
6.171253717 × 10101
6.2%
6.160920476 × 10101
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
5,33203E+11
 
1
5,58335E+11
 
1
5,89586E+11
 
1
6,56756E+11
 
1
7,05917E+11
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row5,33203E+11
2nd row5,58335E+11
3rd row5,89586E+11
4th row6,56756E+11
5th row7,05917E+11

Common Values

ValueCountFrequency (%)
5,33203E+111
 
6.2%
5,58335E+111
 
6.2%
5,89586E+111
 
6.2%
6,56756E+111
 
6.2%
7,05917E+111
 
6.2%
7,38005E+111
 
6.2%
7,52288E+111
 
6.2%
7,25205E+111
 
6.2%
6,79229E+111
 
6.2%
6,47789E+111
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:34.393174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,33203e+111
 
6.2%
5,58335e+111
 
6.2%
5,89586e+111
 
6.2%
6,56756e+111
 
6.2%
7,05917e+111
 
6.2%
7,38005e+111
 
6.2%
7,52288e+111
 
6.2%
7,25205e+111
 
6.2%
6,79229e+111
 
6.2%
6,47789e+111
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
134
19.4%
,16
9.1%
E16
9.1%
+16
9.1%
515
8.6%
314
8.0%
713
 
7.4%
612
 
6.9%
811
 
6.3%
210
 
5.7%
Other values (3)18
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number127
72.6%
Other Punctuation16
 
9.1%
Uppercase Letter16
 
9.1%
Math Symbol16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
134
26.8%
515
11.8%
314
11.0%
713
 
10.2%
612
 
9.4%
811
 
8.7%
210
 
7.9%
97
 
5.5%
46
 
4.7%
05
 
3.9%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common159
90.9%
Latin16
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
134
21.4%
,16
10.1%
+16
10.1%
515
9.4%
314
8.8%
713
 
8.2%
612
 
7.5%
811
 
6.9%
210
 
6.3%
97
 
4.4%
Other values (2)11
 
6.9%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
19.4%
,16
9.1%
E16
9.1%
+16
9.1%
515
8.6%
314
8.0%
713
 
7.4%
612
 
6.9%
811
 
6.3%
210
 
5.7%
Other values (3)18
10.3%

Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.248784016 × 1010
Minimum5.163353922 × 1010
Maximum7.344803201 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:34.477501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.163353922 × 1010
5-th percentile5.290388053 × 1010
Q15.864287995 × 1010
median6.390841841 × 1010
Q36.573175381 × 1010
95-th percentile7.304856376 × 1010
Maximum7.344803201 × 1010
Range2.18144928 × 1010
Interquartile range (IQR)7088873855

Descriptive statistics

Standard deviation6225046193
Coefficient of variation (CV)0.09962012092
Kurtosis-0.307038601
Mean6.248784016 × 1010
Median Absolute Deviation (MAD)3502631468
Skewness0.01846995269
Sum9.998054426 × 1011
Variance3.875120011 × 1019
MonotonicityNot monotonic
2022-06-22T15:25:34.565580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
6.165363504 × 10101
 
6.2%
6.421761953 × 10101
 
6.2%
7.344803201 × 10101
 
6.2%
7.291540767 × 10101
 
6.2%
6.401050667 × 10101
 
6.2%
6.397911197 × 10101
 
6.2%
6.656955257 × 10101
 
6.2%
6.545248755 × 10101
 
6.2%
6.383772486 × 10101
 
6.2%
6.699546865 × 10101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
5.163353922 × 10101
6.2%
5.332732763 × 10101
6.2%
5.568022822 × 10101
6.2%
5.685613307 × 10101
6.2%
5.923846225 × 10101
6.2%
5.999020572 × 10101
6.2%
6.165363504 × 10101
6.2%
6.383772486 × 10101
6.2%
6.397911197 × 10101
6.2%
6.401050667 × 10101
6.2%
ValueCountFrequency (%)
7.344803201 × 10101
6.2%
7.291540767 × 10101
6.2%
6.699546865 × 10101
6.2%
6.656955257 × 10101
6.2%
6.545248755 × 10101
6.2%
6.421761953 × 10101
6.2%
6.401050667 × 10101
6.2%
6.397911197 × 10101
6.2%
6.383772486 × 10101
6.2%
6.165363504 × 10101
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.643544072 × 1010
Minimum1.358861974 × 1010
Maximum3.69362099 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:34.648986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.358861974 × 1010
5-th percentile1.570080541 × 1010
Q12.328999968 × 1010
median2.577784055 × 1010
Q33.271387633 × 1010
95-th percentile3.473626083 × 1010
Maximum3.69362099 × 1010
Range2.334759016 × 1010
Interquartile range (IQR)9423876655

Descriptive statistics

Standard deviation6706938632
Coefficient of variation (CV)0.2537101122
Kurtosis-0.6233271942
Mean2.643544072 × 1010
Median Absolute Deviation (MAD)5666787667
Skewness-0.2798080062
Sum4.229670516 × 1011
Variance4.498302581 × 1019
MonotonicityNot monotonic
2022-06-22T15:25:34.750219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.358861974 × 10101
 
6.2%
1.640486731 × 10101
 
6.2%
2.048575802 × 10101
 
6.2%
2.445290304 × 10101
 
6.2%
2.564880991 × 10101
 
6.2%
3.400294447 × 10101
 
6.2%
3.69362099 × 10101
 
6.2%
3.398700507 × 10101
 
6.2%
3.287478723 × 10101
 
6.2%
3.266023937 × 10101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
1.358861974 × 10101
6.2%
1.640486731 × 10101
6.2%
1.973634776 × 10101
6.2%
2.048575802 × 10101
6.2%
2.42247469 × 10101
6.2%
2.445290304 × 10101
6.2%
2.461770168 × 10101
6.2%
2.564880991 × 10101
6.2%
2.59068712 × 10101
6.2%
2.817740687 × 10101
6.2%
ValueCountFrequency (%)
3.69362099 × 10101
6.2%
3.400294447 × 10101
6.2%
3.398700507 × 10101
6.2%
3.287478723 × 10101
6.2%
3.266023937 × 10101
6.2%
2.92618331 × 10101
6.2%
2.817740687 × 10101
6.2%
2.59068712 × 10101
6.2%
2.564880991 × 10101
6.2%
2.461770168 × 10101
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.821056516 × 1010
Minimum2.307231292 × 1010
Maximum7.28874466 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:34.833209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.307231292 × 1010
5-th percentile2.37320242 × 1010
Q13.729220998 × 1010
median4.85186723 × 1010
Q35.86180758 × 1010
95-th percentile7.182353704 × 1010
Maximum7.28874466 × 1010
Range4.981513368 × 1010
Interquartile range (IQR)2.132586583 × 1010

Descriptive statistics

Standard deviation1.58720992 × 1010
Coefficient of variation (CV)0.3292244997
Kurtosis-0.8641216984
Mean4.821056516 × 1010
Median Absolute Deviation (MAD)1.265640674 × 1010
Skewness-0.07592068579
Sum7.713690426 × 1011
Variance2.519235329 × 1020
MonotonicityNot monotonic
2022-06-22T15:25:34.927806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.307231292 × 10101
 
6.2%
2.395192796 × 10101
 
6.2%
2.825477345 × 10101
 
6.2%
3.300237673 × 10101
 
6.2%
3.872215439 × 10101
 
6.2%
4.609044566 × 10101
 
6.2%
4.963381579 × 10101
 
6.2%
4.721692005 × 10101
 
6.2%
4.74035288 × 10101
 
6.2%
5.091409628 × 10101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.307231292 × 10101
6.2%
2.395192796 × 10101
6.2%
2.825477345 × 10101
6.2%
3.300237673 × 10101
6.2%
3.872215439 × 10101
6.2%
4.609044566 × 10101
6.2%
4.721692005 × 10101
6.2%
4.74035288 × 10101
6.2%
4.963381579 × 10101
6.2%
5.091409628 × 10101
6.2%
ValueCountFrequency (%)
7.28874466 × 10101
6.2%
7.146890052 × 10101
6.2%
6.625780172 × 10101
6.2%
6.455943528 × 10101
6.2%
5.663762264 × 10101
6.2%
5.129548375 × 10101
6.2%
5.091409628 × 10101
6.2%
4.963381579 × 10101
6.2%
4.74035288 × 10101
6.2%
4.721692005 × 10101
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5183835729
Minimum3035131019
Maximum6758693845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:35.534489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3035131019
5-th percentile3101373988
Q14469338966
median5402856776
Q35908735099
95-th percentile6677779652
Maximum6758693845
Range3723562826
Interquartile range (IQR)1439396132

Descriptive statistics

Standard deviation1131588905
Coefficient of variation (CV)0.2182918141
Kurtosis-0.3351441505
Mean5183835729
Median Absolute Deviation (MAD)801071334
Skewness-0.5269485928
Sum8.294137166 × 1010
Variance1.280493451 × 1018
MonotonicityNot monotonic
2022-06-22T15:25:35.634220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
31234549781
 
6.2%
30351310191
 
6.2%
42230376461
 
6.2%
43346541241
 
6.2%
45142339141
 
6.2%
47890313391
 
6.2%
54984585421
 
6.2%
57170355751
 
6.2%
64731443781
 
6.2%
67586938451
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
30351310191
6.2%
31234549781
6.2%
42230376461
6.2%
43346541241
6.2%
45142339141
6.2%
47890313391
6.2%
50620766461
6.2%
53368757401
6.2%
54688378121
6.2%
54984585421
6.2%
ValueCountFrequency (%)
67586938451
6.2%
66508082541
6.2%
64731443781
6.2%
61163765821
6.2%
58395212711
6.2%
57170355751
6.2%
54984585421
6.2%
54688378121
6.2%
53368757401
6.2%
50620766461
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3699323606
Minimum3139312128
Maximum4594154078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-06-22T15:25:35.728808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3139312128
5-th percentile3147949692
Q13475584970
median3579235714
Q33948915939
95-th percentile4515731092
Maximum4594154078
Range1454841950
Interquartile range (IQR)473330968.8

Descriptive statistics

Standard deviation438933417
Coefficient of variation (CV)0.118652344
Kurtosis-0.08989380957
Mean3699323606
Median Absolute Deviation (MAD)218405155
Skewness0.8567880573
Sum5.918917769 × 1010
Variance1.926625446 × 1017
MonotonicityNot monotonic
2022-06-22T15:25:35.819775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
35669638151
 
6.2%
35061396581
 
6.2%
35256842441
 
6.2%
32859250811
 
6.2%
35926877021
 
6.2%
41881680921
 
6.2%
45941540781
 
6.2%
44895900961
 
6.2%
41182084831
 
6.2%
38924850911
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
31393121281
6.2%
31508288801
6.2%
32859250811
6.2%
34357360371
6.2%
34888679481
6.2%
35061396581
6.2%
35256842441
6.2%
35669638151
6.2%
35915076131
6.2%
35926877021
6.2%
ValueCountFrequency (%)
45941540781
6.2%
44895900961
6.2%
41881680921
6.2%
41182084831
6.2%
38924850911
6.2%
36229187431
6.2%
35926877021
6.2%
35915076131
6.2%
35669638151
6.2%
35256842441
6.2%

Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
42789953651
 
1
51453373234
 
1
62136590755
 
1
78840802820
 
1
96601666753
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row42789953651
2nd row51453373234
3rd row62136590755
4th row78840802820
5th row96601666753

Common Values

ValueCountFrequency (%)
427899536511
 
6.2%
514533732341
 
6.2%
621365907551
 
6.2%
788408028201
 
6.2%
966016667531
 
6.2%
1,05523E+111
 
6.2%
1,25286E+111
 
6.2%
1,45128E+111
 
6.2%
1,6407E+111
 
6.2%
1,82109E+111
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:35.903303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
427899536511
 
6.2%
514533732341
 
6.2%
621365907551
 
6.2%
788408028201
 
6.2%
966016667531
 
6.2%
1,05523e+111
 
6.2%
1,25286e+111
 
6.2%
1,45128e+111
 
6.2%
1,6407e+111
 
6.2%
1,82109e+111
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
137
21.1%
317
9.7%
216
9.1%
516
9.1%
611
 
6.3%
011
 
6.3%
,11
 
6.3%
E11
 
6.3%
+11
 
6.3%
410
 
5.7%
Other values (3)24
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number142
81.1%
Other Punctuation11
 
6.3%
Uppercase Letter11
 
6.3%
Math Symbol11
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
137
26.1%
317
12.0%
216
11.3%
516
11.3%
611
 
7.7%
011
 
7.7%
410
 
7.0%
810
 
7.0%
98
 
5.6%
76
 
4.2%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%
Uppercase Letter
ValueCountFrequency (%)
E11
100.0%
Math Symbol
ValueCountFrequency (%)
+11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common164
93.7%
Latin11
 
6.3%

Most frequent character per script

Common
ValueCountFrequency (%)
137
22.6%
317
10.4%
216
9.8%
516
9.8%
611
 
6.7%
011
 
6.7%
,11
 
6.7%
+11
 
6.7%
410
 
6.1%
810
 
6.1%
Other values (2)14
 
8.5%
Latin
ValueCountFrequency (%)
E11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
137
21.1%
317
9.7%
216
9.1%
516
9.1%
611
 
6.3%
011
 
6.3%
,11
 
6.3%
E11
 
6.3%
+11
 
6.3%
410
 
5.7%
Other values (3)24
13.7%

Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
1,15955E+12
 
1
1,20604E+12
 
1
1,33768E+12
 
1
1,50972E+12
 
1
1,56427E+12
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row1,15955E+12
2nd row1,20604E+12
3rd row1,33768E+12
4th row1,50972E+12
5th row1,56427E+12

Common Values

ValueCountFrequency (%)
1,15955E+121
 
6.2%
1,20604E+121
 
6.2%
1,33768E+121
 
6.2%
1,50972E+121
 
6.2%
1,56427E+121
 
6.2%
1,6484E+121
 
6.2%
1,75048E+121
 
6.2%
1,75775E+121
 
6.2%
1,75559E+121
 
6.2%
1,75419E+121
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:35.985303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,15955e+121
 
6.2%
1,20604e+121
 
6.2%
1,33768e+121
 
6.2%
1,50972e+121
 
6.2%
1,56427e+121
 
6.2%
1,6484e+121
 
6.2%
1,75048e+121
 
6.2%
1,75775e+121
 
6.2%
1,75559e+121
 
6.2%
1,75419e+121
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
137
21.1%
221
12.0%
,16
9.1%
516
9.1%
E16
9.1%
+16
9.1%
911
 
6.3%
711
 
6.3%
48
 
4.6%
88
 
4.6%
Other values (3)15
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number127
72.6%
Other Punctuation16
 
9.1%
Uppercase Letter16
 
9.1%
Math Symbol16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
137
29.1%
221
16.5%
516
12.6%
911
 
8.7%
711
 
8.7%
48
 
6.3%
88
 
6.3%
66
 
4.7%
05
 
3.9%
34
 
3.1%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Uppercase Letter
ValueCountFrequency (%)
E16
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common159
90.9%
Latin16
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
137
23.3%
221
13.2%
,16
10.1%
516
10.1%
+16
10.1%
911
 
6.9%
711
 
6.9%
48
 
5.0%
88
 
5.0%
66
 
3.8%
Other values (2)9
 
5.7%
Latin
ValueCountFrequency (%)
E16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
137
21.1%
221
12.0%
,16
9.1%
516
9.1%
E16
9.1%
+16
9.1%
911
 
6.3%
711
 
6.3%
48
 
4.6%
88
 
4.6%
Other values (3)15
8.6%
Distinct11
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Memory size256.0 B
8,3
9,5
8,2
8,1
8,4
Other values (6)

Length

Max length3
Median length3
Mean length2.875
Min length1

Characters and Unicode

Total characters46
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)56.2%

Sample

1st row8,3
2nd row8,2
3rd row8,3
4th row8,3
5th row8,1

Common Values

ValueCountFrequency (%)
8,35
31.2%
9,52
 
12.5%
8,21
 
6.2%
8,11
 
6.2%
8,41
 
6.2%
8,51
 
6.2%
8,81
 
6.2%
91
 
6.2%
9,61
 
6.2%
9,41
 
6.2%

Length

2022-06-22T15:25:36.075944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8,35
31.2%
9,52
 
12.5%
8,21
 
6.2%
8,11
 
6.2%
8,41
 
6.2%
8,51
 
6.2%
8,81
 
6.2%
91
 
6.2%
9,61
 
6.2%
9,41
 
6.2%

Most occurring characters

ValueCountFrequency (%)
,15
32.6%
811
23.9%
36
 
13.0%
96
 
13.0%
53
 
6.5%
42
 
4.3%
21
 
2.2%
11
 
2.2%
61
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number31
67.4%
Other Punctuation15
32.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
811
35.5%
36
19.4%
96
19.4%
53
 
9.7%
42
 
6.5%
21
 
3.2%
11
 
3.2%
61
 
3.2%
Other Punctuation
ValueCountFrequency (%)
,15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common46
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,15
32.6%
811
23.9%
36
 
13.0%
96
 
13.0%
53
 
6.5%
42
 
4.3%
21
 
2.2%
11
 
2.2%
61
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII46
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,15
32.6%
811
23.9%
36
 
13.0%
96
 
13.0%
53
 
6.5%
42
 
4.3%
21
 
2.2%
11
 
2.2%
61
 
2.2%
Distinct12
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
12,8
12,9
12,4
13,1
12,7
Other values (7)

Length

Max length4
Median length4
Mean length3.875
Min length2

Characters and Unicode

Total characters62
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)56.2%

Sample

1st row12,8
2nd row13,1
3rd row12,8
4th row12,9
5th row12,8

Common Values

ValueCountFrequency (%)
12,83
18.8%
12,92
12.5%
12,42
12.5%
13,11
 
6.2%
12,71
 
6.2%
12,61
 
6.2%
121
 
6.2%
11,81
 
6.2%
11,51
 
6.2%
11,31
 
6.2%
Other values (2)2
12.5%

Length

2022-06-22T15:25:36.169618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12,83
18.8%
12,92
12.5%
12,42
12.5%
13,11
 
6.2%
12,71
 
6.2%
12,61
 
6.2%
121
 
6.2%
11,81
 
6.2%
11,51
 
6.2%
11,31
 
6.2%
Other values (2)2
12.5%

Most occurring characters

ValueCountFrequency (%)
121
33.9%
,15
24.2%
211
17.7%
84
 
6.5%
93
 
4.8%
42
 
3.2%
32
 
3.2%
71
 
1.6%
61
 
1.6%
51
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number47
75.8%
Other Punctuation15
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
121
44.7%
211
23.4%
84
 
8.5%
93
 
6.4%
42
 
4.3%
32
 
4.3%
71
 
2.1%
61
 
2.1%
51
 
2.1%
01
 
2.1%
Other Punctuation
ValueCountFrequency (%)
,15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common62
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
121
33.9%
,15
24.2%
211
17.7%
84
 
6.5%
93
 
4.8%
42
 
3.2%
32
 
3.2%
71
 
1.6%
61
 
1.6%
51
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII62
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
33.9%
,15
24.2%
211
17.7%
84
 
6.5%
93
 
4.8%
42
 
3.2%
32
 
3.2%
71
 
1.6%
61
 
1.6%
51
 
1.6%
Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size256.0 B
9,6
9,7
9,8
9,5
9,2
Other values (9)

Length

Max length3
Median length3
Mean length2.625
Min length1

Characters and Unicode

Total characters42
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)81.2%

Sample

1st row9,6
2nd row9,6
3rd row9,7
4th row9,8
5th row9,6

Common Values

ValueCountFrequency (%)
9,63
18.8%
9,71
 
6.2%
9,81
 
6.2%
9,51
 
6.2%
9,21
 
6.2%
91
 
6.2%
8,51
 
6.2%
8,31
 
6.2%
81
 
6.2%
7,81
 
6.2%
Other values (4)4
25.0%

Length

2022-06-22T15:25:36.260071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9,63
18.8%
9,71
 
6.2%
9,81
 
6.2%
9,51
 
6.2%
9,21
 
6.2%
91
 
6.2%
8,51
 
6.2%
8,31
 
6.2%
81
 
6.2%
7,81
 
6.2%
Other values (4)4
25.0%

Most occurring characters

ValueCountFrequency (%)
,13
31.0%
98
19.0%
86
14.3%
65
 
11.9%
75
 
11.9%
52
 
4.8%
32
 
4.8%
21
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29
69.0%
Other Punctuation13
31.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
98
27.6%
86
20.7%
65
17.2%
75
17.2%
52
 
6.9%
32
 
6.9%
21
 
3.4%
Other Punctuation
ValueCountFrequency (%)
,13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common42
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,13
31.0%
98
19.0%
86
14.3%
65
 
11.9%
75
 
11.9%
52
 
4.8%
32
 
4.8%
21
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII42
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,13
31.0%
98
19.0%
86
14.3%
65
 
11.9%
75
 
11.9%
52
 
4.8%
32
 
4.8%
21
 
2.4%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct13
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Memory size256.0 B
8,5
8,2
8
8,41
8,65
Other values (8)

Length

Max length4
Median length3
Mean length2.8125
Min length1

Characters and Unicode

Total characters45
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)62.5%

Sample

1st row8,41
2nd row8,65
3rd row8,63
4th row8,7
5th row8,5

Common Values

ValueCountFrequency (%)
8,52
12.5%
8,22
12.5%
82
12.5%
8,411
 
6.2%
8,651
 
6.2%
8,631
 
6.2%
8,71
 
6.2%
8,31
 
6.2%
7,81
 
6.2%
7,61
 
6.2%
Other values (3)3
18.8%

Length

2022-06-22T15:25:36.348000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8,52
12.5%
8,22
12.5%
82
12.5%
8,411
 
6.2%
8,651
 
6.2%
8,631
 
6.2%
8,71
 
6.2%
8,31
 
6.2%
7,81
 
6.2%
7,61
 
6.2%
Other values (3)3
18.8%

Most occurring characters

ValueCountFrequency (%)
813
28.9%
,13
28.9%
75
 
11.1%
64
 
8.9%
53
 
6.7%
22
 
4.4%
42
 
4.4%
32
 
4.4%
11
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number32
71.1%
Other Punctuation13
28.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
813
40.6%
75
 
15.6%
64
 
12.5%
53
 
9.4%
22
 
6.2%
42
 
6.2%
32
 
6.2%
11
 
3.1%
Other Punctuation
ValueCountFrequency (%)
,13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
813
28.9%
,13
28.9%
75
 
11.1%
64
 
8.9%
53
 
6.7%
22
 
4.4%
42
 
4.4%
32
 
4.4%
11
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
813
28.9%
,13
28.9%
75
 
11.1%
64
 
8.9%
53
 
6.7%
22
 
4.4%
42
 
4.4%
32
 
4.4%
11
 
2.2%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct13
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Memory size256.0 B
10,6
11
10,8
10,9
11,2
Other values (8)

Length

Max length4
Median length4
Mean length3.625
Min length2

Characters and Unicode

Total characters58
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)62.5%

Sample

1st row10,6
2nd row10,9
3rd row11,2
4th row11,4
5th row11,3

Common Values

ValueCountFrequency (%)
10,62
12.5%
112
12.5%
10,82
12.5%
10,91
 
6.2%
11,21
 
6.2%
11,41
 
6.2%
11,31
 
6.2%
11,11
 
6.2%
10,71
 
6.2%
10,31
 
6.2%
Other values (3)3
18.8%

Length

2022-06-22T15:25:36.434867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10,62
12.5%
112
12.5%
10,82
12.5%
10,91
 
6.2%
11,21
 
6.2%
11,41
 
6.2%
11,31
 
6.2%
11,11
 
6.2%
10,71
 
6.2%
10,31
 
6.2%
Other values (3)3
18.8%

Most occurring characters

ValueCountFrequency (%)
122
37.9%
,14
24.1%
08
 
13.8%
94
 
6.9%
62
 
3.4%
82
 
3.4%
42
 
3.4%
32
 
3.4%
21
 
1.7%
71
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number44
75.9%
Other Punctuation14
 
24.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
122
50.0%
08
 
18.2%
94
 
9.1%
62
 
4.5%
82
 
4.5%
42
 
4.5%
32
 
4.5%
21
 
2.3%
71
 
2.3%
Other Punctuation
ValueCountFrequency (%)
,14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common58
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
122
37.9%
,14
24.1%
08
 
13.8%
94
 
6.9%
62
 
3.4%
82
 
3.4%
42
 
3.4%
32
 
3.4%
21
 
1.7%
71
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII58
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122
37.9%
,14
24.1%
08
 
13.8%
94
 
6.9%
62
 
3.4%
82
 
3.4%
42
 
3.4%
32
 
3.4%
21
 
1.7%
71
 
1.7%
Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size256.0 B
13,3
10,2
10,3
11,3
12
Other values (9)

Length

Max length4
Median length4
Mean length3.8125
Min length2

Characters and Unicode

Total characters61
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)81.2%

Sample

1st row10,2
2nd row10,3
3rd row11,3
4th row12
5th row12,3

Common Values

ValueCountFrequency (%)
13,33
18.8%
10,21
 
6.2%
10,31
 
6.2%
11,31
 
6.2%
121
 
6.2%
12,31
 
6.2%
12,51
 
6.2%
12,61
 
6.2%
13,21
 
6.2%
12,91
 
6.2%
Other values (4)4
25.0%

Length

2022-06-22T15:25:36.526784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13,33
18.8%
10,21
 
6.2%
10,31
 
6.2%
11,31
 
6.2%
121
 
6.2%
12,31
 
6.2%
12,51
 
6.2%
12,61
 
6.2%
13,21
 
6.2%
12,91
 
6.2%
Other values (4)4
25.0%

Most occurring characters

ValueCountFrequency (%)
118
29.5%
,15
24.6%
310
16.4%
27
 
11.5%
04
 
6.6%
93
 
4.9%
52
 
3.3%
61
 
1.6%
81
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
75.4%
Other Punctuation15
 
24.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
118
39.1%
310
21.7%
27
 
15.2%
04
 
8.7%
93
 
6.5%
52
 
4.3%
61
 
2.2%
81
 
2.2%
Other Punctuation
ValueCountFrequency (%)
,15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common61
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
118
29.5%
,15
24.6%
310
16.4%
27
 
11.5%
04
 
6.6%
93
 
4.9%
52
 
3.3%
61
 
1.6%
81
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII61
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118
29.5%
,15
24.6%
310
16.4%
27
 
11.5%
04
 
6.6%
93
 
4.9%
52
 
3.3%
61
 
1.6%
81
 
1.6%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct13
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Memory size256.0 B
14
14,3
12,4
13,5
13
Other values (8)

Length

Max length4
Median length4
Mean length3.625
Min length2

Characters and Unicode

Total characters58
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)62.5%

Sample

1st row14
2nd row14,3
3rd row14,3
4th row14
5th row13,5

Common Values

ValueCountFrequency (%)
142
12.5%
14,32
12.5%
12,42
12.5%
13,51
 
6.2%
131
 
6.2%
12,71
 
6.2%
12,61
 
6.2%
12,51
 
6.2%
12,21
 
6.2%
11,81
 
6.2%
Other values (3)3
18.8%

Length

2022-06-22T15:25:36.619243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
142
12.5%
14,32
12.5%
12,42
12.5%
13,51
 
6.2%
131
 
6.2%
12,71
 
6.2%
12,61
 
6.2%
12,51
 
6.2%
12,21
 
6.2%
11,81
 
6.2%
Other values (3)3
18.8%

Most occurring characters

ValueCountFrequency (%)
119
32.8%
,13
22.4%
47
 
12.1%
27
 
12.1%
34
 
6.9%
52
 
3.4%
62
 
3.4%
71
 
1.7%
81
 
1.7%
01
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number45
77.6%
Other Punctuation13
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
119
42.2%
47
 
15.6%
27
 
15.6%
34
 
8.9%
52
 
4.4%
62
 
4.4%
71
 
2.2%
81
 
2.2%
01
 
2.2%
91
 
2.2%
Other Punctuation
ValueCountFrequency (%)
,13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common58
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
119
32.8%
,13
22.4%
47
 
12.1%
27
 
12.1%
34
 
6.9%
52
 
3.4%
62
 
3.4%
71
 
1.7%
81
 
1.7%
01
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII58
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
119
32.8%
,13
22.4%
47
 
12.1%
27
 
12.1%
34
 
6.9%
52
 
3.4%
62
 
3.4%
71
 
1.7%
81
 
1.7%
01
 
1.7%
Distinct13
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Memory size256.0 B
12
12,9
12,8
12,3
12,6
Other values (8)

Length

Max length4
Median length4
Mean length3.625
Min length2

Characters and Unicode

Total characters58
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)62.5%

Sample

1st row12
2nd row12,3
3rd row12,6
4th row12,9
5th row12,7

Common Values

ValueCountFrequency (%)
122
12.5%
12,92
12.5%
12,82
12.5%
12,31
 
6.2%
12,61
 
6.2%
12,71
 
6.2%
12,11
 
6.2%
11,91
 
6.2%
11,81
 
6.2%
11,41
 
6.2%
Other values (3)3
18.8%

Length

2022-06-22T15:25:36.712362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
122
12.5%
12,92
12.5%
12,82
12.5%
12,31
 
6.2%
12,61
 
6.2%
12,71
 
6.2%
12,11
 
6.2%
11,91
 
6.2%
11,81
 
6.2%
11,41
 
6.2%
Other values (3)3
18.8%

Most occurring characters

ValueCountFrequency (%)
121
36.2%
,13
22.4%
211
19.0%
93
 
5.2%
83
 
5.2%
72
 
3.4%
02
 
3.4%
31
 
1.7%
61
 
1.7%
41
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number45
77.6%
Other Punctuation13
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
121
46.7%
211
24.4%
93
 
6.7%
83
 
6.7%
72
 
4.4%
02
 
4.4%
31
 
2.2%
61
 
2.2%
41
 
2.2%
Other Punctuation
ValueCountFrequency (%)
,13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common58
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
121
36.2%
,13
22.4%
211
19.0%
93
 
5.2%
83
 
5.2%
72
 
3.4%
02
 
3.4%
31
 
1.7%
61
 
1.7%
41
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII58
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
36.2%
,13
22.4%
211
19.0%
93
 
5.2%
83
 
5.2%
72
 
3.4%
02
 
3.4%
31
 
1.7%
61
 
1.7%
41
 
1.7%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
17,243
 
1
16,747
 
1
16,306
 
1
15,921
 
1
15,595
 
1
Other values (11)
11 

Length

Max length6
Median length6
Mean length5.875
Min length5

Characters and Unicode

Total characters94
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row17,243
2nd row16,747
3rd row16,306
4th row15,921
5th row15,595

Common Values

ValueCountFrequency (%)
17,2431
 
6.2%
16,7471
 
6.2%
16,3061
 
6.2%
15,9211
 
6.2%
15,5951
 
6.2%
15,3271
 
6.2%
15,111
 
6.2%
14,931
 
6.2%
14,7721
 
6.2%
14,6241
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:36.800178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17,2431
 
6.2%
16,7471
 
6.2%
16,3061
 
6.2%
15,9211
 
6.2%
15,5951
 
6.2%
15,3271
 
6.2%
15,111
 
6.2%
14,931
 
6.2%
14,7721
 
6.2%
14,6241
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
120
21.3%
,16
17.0%
412
12.8%
310
10.6%
79
9.6%
28
 
8.5%
57
 
7.4%
65
 
5.3%
94
 
4.3%
03
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number78
83.0%
Other Punctuation16
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
120
25.6%
412
15.4%
310
12.8%
79
11.5%
28
 
10.3%
57
 
9.0%
65
 
6.4%
94
 
5.1%
03
 
3.8%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common94
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
120
21.3%
,16
17.0%
412
12.8%
310
10.6%
79
9.6%
28
 
8.5%
57
 
7.4%
65
 
5.3%
94
 
4.3%
03
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII94
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
21.3%
,16
17.0%
412
12.8%
310
10.6%
79
9.6%
28
 
8.5%
57
 
7.4%
65
 
5.3%
94
 
4.3%
03
 
3.2%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
24,087
 
1
23,564
 
1
22,996
 
1
22,39
 
1
21,755
 
1
Other values (11)
11 

Length

Max length6
Median length6
Mean length5.9375
Min length5

Characters and Unicode

Total characters95
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row24,087
2nd row23,564
3rd row22,996
4th row22,39
5th row21,755

Common Values

ValueCountFrequency (%)
24,0871
 
6.2%
23,5641
 
6.2%
22,9961
 
6.2%
22,391
 
6.2%
21,7551
 
6.2%
21,1141
 
6.2%
20,4951
 
6.2%
19,9231
 
6.2%
19,4161
 
6.2%
18,9841
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:36.883351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
24,0871
 
6.2%
23,5641
 
6.2%
22,9961
 
6.2%
22,391
 
6.2%
21,7551
 
6.2%
21,1141
 
6.2%
20,4951
 
6.2%
19,9231
 
6.2%
19,4161
 
6.2%
18,9841
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
,16
16.8%
114
14.7%
212
12.6%
49
9.5%
88
8.4%
98
8.4%
77
7.4%
37
7.4%
56
 
6.3%
65
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79
83.2%
Other Punctuation16
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
114
17.7%
212
15.2%
49
11.4%
88
10.1%
98
10.1%
77
8.9%
37
8.9%
56
7.6%
65
 
6.3%
03
 
3.8%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common95
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,16
16.8%
114
14.7%
212
12.6%
49
9.5%
88
8.4%
98
8.4%
77
7.4%
37
7.4%
56
 
6.3%
65
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII95
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,16
16.8%
114
14.7%
212
12.6%
49
9.5%
88
8.4%
98
8.4%
77
7.4%
37
7.4%
56
 
6.3%
65
 
5.3%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
21,741
 
1
21,343
 
1
20,973
 
1
20,635
 
1
20,327
 
1
Other values (11)
11 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters96
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row21,741
2nd row21,343
3rd row20,973
4th row20,635
5th row20,327

Common Values

ValueCountFrequency (%)
21,7411
 
6.2%
21,3431
 
6.2%
20,9731
 
6.2%
20,6351
 
6.2%
20,3271
 
6.2%
20,0421
 
6.2%
19,7661
 
6.2%
19,4881
 
6.2%
19,1981
 
6.2%
18,8921
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:36.967166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21,7411
 
6.2%
21,3431
 
6.2%
20,9731
 
6.2%
20,6351
 
6.2%
20,3271
 
6.2%
20,0421
 
6.2%
19,7661
 
6.2%
19,4881
 
6.2%
19,1981
 
6.2%
18,8921
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
,16
16.7%
115
15.6%
212
12.5%
710
10.4%
08
8.3%
98
8.3%
88
8.3%
36
 
6.2%
45
 
5.2%
64
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80
83.3%
Other Punctuation16
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115
18.8%
212
15.0%
710
12.5%
08
10.0%
98
10.0%
88
10.0%
36
 
7.5%
45
 
6.2%
64
 
5.0%
54
 
5.0%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,16
16.7%
115
15.6%
212
12.5%
710
10.4%
08
8.3%
98
8.3%
88
8.3%
36
 
6.2%
45
 
5.2%
64
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII96
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,16
16.7%
115
15.6%
212
12.5%
710
10.4%
08
8.3%
98
8.3%
88
8.3%
36
 
6.2%
45
 
5.2%
64
 
4.2%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
23,073
 
1
23,238
 
1
23,367
 
1
23,433
 
1
23,417
 
1
Other values (11)
11 

Length

Max length6
Median length6
Mean length5.9375
Min length5

Characters and Unicode

Total characters95
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row23,073
2nd row23,238
3rd row23,367
4th row23,433
5th row23,417

Common Values

ValueCountFrequency (%)
23,0731
 
6.2%
23,2381
 
6.2%
23,3671
 
6.2%
23,4331
 
6.2%
23,4171
 
6.2%
23,3051
 
6.2%
23,0971
 
6.2%
22,8151
 
6.2%
22,4831
 
6.2%
22,1131
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:37.056250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23,0731
 
6.2%
23,2381
 
6.2%
23,3671
 
6.2%
23,4331
 
6.2%
23,4171
 
6.2%
23,3051
 
6.2%
23,0971
 
6.2%
22,8151
 
6.2%
22,4831
 
6.2%
22,1131
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
220
21.1%
317
17.9%
,16
16.8%
111
11.6%
07
 
7.4%
77
 
7.4%
95
 
5.3%
84
 
4.2%
44
 
4.2%
53
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79
83.2%
Other Punctuation16
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
220
25.3%
317
21.5%
111
13.9%
07
 
8.9%
77
 
8.9%
95
 
6.3%
84
 
5.1%
44
 
5.1%
53
 
3.8%
61
 
1.3%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common95
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
220
21.1%
317
17.9%
,16
16.8%
111
11.6%
07
 
7.4%
77
 
7.4%
95
 
5.3%
84
 
4.2%
44
 
4.2%
53
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII95
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220
21.1%
317
17.9%
,16
16.8%
111
11.6%
07
 
7.4%
77
 
7.4%
95
 
5.3%
84
 
4.2%
44
 
4.2%
53
 
3.2%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
12,4
 
1
12,09
 
1
12,1
 
1
12,14
 
1
11,95
 
1
Other values (11)
11 

Length

Max length5
Median length5
Mean length4.75
Min length4

Characters and Unicode

Total characters76
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row12,4
2nd row12,09
3rd row12,1
4th row12,14
5th row11,95

Common Values

ValueCountFrequency (%)
12,41
 
6.2%
12,091
 
6.2%
12,11
 
6.2%
12,141
 
6.2%
11,951
 
6.2%
11,91
 
6.2%
13,271
 
6.2%
14,571
 
6.2%
13,031
 
6.2%
13,831
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:37.142896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12,41
 
6.2%
12,091
 
6.2%
12,11
 
6.2%
12,141
 
6.2%
11,951
 
6.2%
11,91
 
6.2%
13,271
 
6.2%
14,571
 
6.2%
13,031
 
6.2%
13,831
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
121
27.6%
,16
21.1%
27
 
9.2%
36
 
7.9%
45
 
6.6%
95
 
6.6%
04
 
5.3%
54
 
5.3%
73
 
3.9%
83
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number60
78.9%
Other Punctuation16
 
21.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
121
35.0%
27
 
11.7%
36
 
10.0%
45
 
8.3%
95
 
8.3%
04
 
6.7%
54
 
6.7%
73
 
5.0%
83
 
5.0%
62
 
3.3%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
121
27.6%
,16
21.1%
27
 
9.2%
36
 
7.9%
45
 
6.6%
95
 
6.6%
04
 
5.3%
54
 
5.3%
73
 
3.9%
83
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII76
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
27.6%
,16
21.1%
27
 
9.2%
36
 
7.9%
45
 
6.6%
95
 
6.6%
04
 
5.3%
54
 
5.3%
73
 
3.9%
83
 
3.9%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
20,55193051
 
1
20,39775655
 
1
20,30710105
 
1
20,19625896
 
1
19,96785143
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters176
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row20,55193051
2nd row20,39775655
3rd row20,30710105
4th row20,19625896
5th row19,96785143

Common Values

ValueCountFrequency (%)
20,551930511
 
6.2%
20,397756551
 
6.2%
20,307101051
 
6.2%
20,196258961
 
6.2%
19,967851431
 
6.2%
19,778701171
 
6.2%
19,854670531
 
6.2%
19,954058181
 
6.2%
19,452447841
 
6.2%
19,464191861
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:37.229619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20,551930511
 
6.2%
20,397756551
 
6.2%
20,307101051
 
6.2%
20,196258961
 
6.2%
19,967851431
 
6.2%
19,778701171
 
6.2%
19,854670531
 
6.2%
19,954058181
 
6.2%
19,452447841
 
6.2%
19,464191861
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
130
17.0%
519
10.8%
717
9.7%
,16
9.1%
916
9.1%
816
9.1%
015
8.5%
214
8.0%
414
8.0%
610
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
130
18.8%
519
11.9%
717
10.6%
916
10.0%
816
10.0%
015
9.4%
214
8.8%
414
8.8%
610
 
6.2%
39
 
5.6%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
130
17.0%
519
10.8%
717
9.7%
,16
9.1%
916
9.1%
816
9.1%
015
8.5%
214
8.0%
414
8.0%
610
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130
17.0%
519
10.8%
717
9.7%
,16
9.1%
916
9.1%
816
9.1%
015
8.5%
214
8.0%
414
8.0%
610
 
5.7%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
78,93170732
 
1
79,13170732
 
1
79,53414634
 
1
79,73658537
 
1
79,83658537
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.8125
Min length10

Characters and Unicode

Total characters173
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row78,93170732
2nd row79,13170732
3rd row79,53414634
4th row79,73658537
5th row79,83658537

Common Values

ValueCountFrequency (%)
78,931707321
 
6.2%
79,131707321
 
6.2%
79,534146341
 
6.2%
79,736585371
 
6.2%
79,836585371
 
6.2%
79,987804881
 
6.2%
80,436585371
 
6.2%
80,539024391
 
6.2%
80,49024391
 
6.2%
81,09024391
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:37.316123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78,931707321
 
6.2%
79,131707321
 
6.2%
79,534146341
 
6.2%
79,736585371
 
6.2%
79,836585371
 
6.2%
79,987804881
 
6.2%
80,436585371
 
6.2%
80,539024391
 
6.2%
80,49024391
 
6.2%
81,09024391
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
924
13.9%
823
13.3%
322
12.7%
,16
9.2%
016
9.2%
416
9.2%
715
8.7%
213
7.5%
110
5.8%
610
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number157
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
924
15.3%
823
14.6%
322
14.0%
016
10.2%
416
10.2%
715
9.6%
213
8.3%
110
6.4%
610
6.4%
58
 
5.1%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
924
13.9%
823
13.3%
322
12.7%
,16
9.2%
016
9.2%
416
9.2%
715
8.7%
213
7.5%
110
5.8%
610
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
924
13.9%
823
13.3%
322
12.7%
,16
9.2%
016
9.2%
416
9.2%
715
8.7%
213
7.5%
110
5.8%
610
5.8%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
80,16341463
 
1
80,81219512
 
1
81,11219512
 
1
81,21463415
 
1
81,41463415
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters174
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row80,16341463
2nd row80,81219512
3rd row81,11219512
4th row81,21463415
5th row81,41463415

Common Values

ValueCountFrequency (%)
80,163414631
 
6.2%
80,812195121
 
6.2%
81,112195121
 
6.2%
81,214634151
 
6.2%
81,414634151
 
6.2%
81,663414631
 
6.2%
82,114634151
 
6.2%
81,968292681
 
6.2%
82,21951221
 
6.2%
82,71951221
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:37.401900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
80,163414631
 
6.2%
80,812195121
 
6.2%
81,112195121
 
6.2%
81,214634151
 
6.2%
81,414634151
 
6.2%
81,663414631
 
6.2%
82,114634151
 
6.2%
81,968292681
 
6.2%
82,21951221
 
6.2%
82,71951221
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
130
17.2%
227
15.5%
821
12.1%
618
10.3%
,16
9.2%
513
7.5%
411
 
6.3%
911
 
6.3%
711
 
6.3%
310
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
130
19.0%
227
17.1%
821
13.3%
618
11.4%
513
8.2%
411
 
7.0%
911
 
7.0%
711
 
7.0%
310
 
6.3%
06
 
3.8%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
130
17.2%
227
15.5%
821
12.1%
618
10.3%
,16
9.2%
513
7.5%
411
 
6.3%
911
 
6.3%
711
 
6.3%
310
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130
17.2%
227
15.5%
821
12.1%
618
10.3%
,16
9.2%
513
7.5%
411
 
6.3%
911
 
6.3%
711
 
6.3%
310
 
5.7%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
80,78292683
 
1
81,28292683
 
1
81,43414634
 
1
81,48536585
 
1
81,63658537
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters174
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row80,78292683
2nd row81,28292683
3rd row81,43414634
4th row81,48536585
5th row81,63658537

Common Values

ValueCountFrequency (%)
80,782926831
 
6.2%
81,282926831
 
6.2%
81,434146341
 
6.2%
81,485365851
 
6.2%
81,636585371
 
6.2%
82,036585371
 
6.2%
82,187804881
 
6.2%
82,239024391
 
6.2%
82,69024391
 
6.2%
83,09024391
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:37.488388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
80,782926831
 
6.2%
81,282926831
 
6.2%
81,434146341
 
6.2%
81,485365851
 
6.2%
81,636585371
 
6.2%
82,036585371
 
6.2%
82,187804881
 
6.2%
82,239024391
 
6.2%
82,69024391
 
6.2%
83,09024391
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
829
16.7%
425
14.4%
324
13.8%
220
11.5%
,16
9.2%
914
8.0%
613
7.5%
011
 
6.3%
59
 
5.2%
18
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
829
18.4%
425
15.8%
324
15.2%
220
12.7%
914
8.9%
613
8.2%
011
 
7.0%
59
 
5.7%
18
 
5.1%
75
 
3.2%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
829
16.7%
425
14.4%
324
13.8%
220
11.5%
,16
9.2%
914
8.0%
613
7.5%
011
 
6.3%
59
 
5.2%
18
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
829
16.7%
425
14.4%
324
13.8%
220
11.5%
,16
9.2%
914
8.0%
613
7.5%
011
 
6.3%
59
 
5.2%
18
 
4.6%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
81,92512195
 
1
82,32195122
 
1
82,50707317
 
1
82,58756098
 
1
82,93146341
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row81,92512195
2nd row82,32195122
3rd row82,50707317
4th row82,58756098
5th row82,93146341

Common Values

ValueCountFrequency (%)
81,925121951
 
6.2%
82,321951221
 
6.2%
82,507073171
 
6.2%
82,587560981
 
6.2%
82,931463411
 
6.2%
82,842682931
 
6.2%
82,591219511
 
6.2%
83,096097561
 
6.2%
83,331951221
 
6.2%
83,587804881
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:37.576981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
81,925121951
 
6.2%
82,321951221
 
6.2%
82,507073171
 
6.2%
82,587560981
 
6.2%
82,931463411
 
6.2%
82,842682931
 
6.2%
82,591219511
 
6.2%
83,096097561
 
6.2%
83,331951221
 
6.2%
83,587804881
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
827
15.4%
118
10.3%
918
10.3%
218
10.3%
,16
9.1%
516
9.1%
315
8.6%
413
7.4%
612
6.9%
011
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
827
17.0%
118
11.3%
918
11.3%
218
11.3%
516
10.1%
315
9.4%
413
8.2%
612
7.5%
011
6.9%
711
6.9%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
827
15.4%
118
10.3%
918
10.3%
218
10.3%
,16
9.1%
516
9.1%
315
8.6%
413
7.4%
612
6.9%
011
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
827
15.4%
118
10.3%
918
10.3%
218
10.3%
,16
9.1%
516
9.1%
315
8.6%
413
7.4%
612
6.9%
011
6.3%
Distinct12
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
81,9
81,74878049
82,04878049
80,19268293
80,34390244
Other values (7)

Length

Max length11
Median length11
Mean length9.25
Min length4

Characters and Unicode

Total characters148
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)56.2%

Sample

1st row80,19268293
2nd row80,34390244
3rd row80,54390244
4th row80,69512195
5th row80,99512195

Common Values

ValueCountFrequency (%)
81,93
18.8%
81,748780492
12.5%
82,048780492
12.5%
80,192682931
 
6.2%
80,343902441
 
6.2%
80,543902441
 
6.2%
80,695121951
 
6.2%
80,995121951
 
6.2%
81,246341461
 
6.2%
81,448780491
 
6.2%
Other values (2)2
12.5%

Length

2022-06-22T15:25:37.667696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
81,93
18.8%
81,748780492
12.5%
82,048780492
12.5%
80,192682931
 
6.2%
80,343902441
 
6.2%
80,543902441
 
6.2%
80,695121951
 
6.2%
80,995121951
 
6.2%
81,246341461
 
6.2%
81,448780491
 
6.2%
Other values (2)2
12.5%

Most occurring characters

ValueCountFrequency (%)
830
20.3%
422
14.9%
918
12.2%
,16
10.8%
115
10.1%
015
10.1%
29
 
6.1%
78
 
5.4%
65
 
3.4%
35
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132
89.2%
Other Punctuation16
 
10.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
830
22.7%
422
16.7%
918
13.6%
115
11.4%
015
11.4%
29
 
6.8%
78
 
6.1%
65
 
3.8%
35
 
3.8%
55
 
3.8%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common148
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
830
20.3%
422
14.9%
918
12.2%
,16
10.8%
115
10.1%
015
10.1%
29
 
6.1%
78
 
5.4%
65
 
3.4%
35
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
830
20.3%
422
14.9%
918
12.2%
,16
10.8%
115
10.1%
015
10.1%
29
 
6.1%
78
 
5.4%
65
 
3.4%
35
 
3.4%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
65,5297561
 
1
66,72756098
 
1
67,58682927
 
1
67,94926829
 
1
68,68463415
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row65,5297561
2nd row66,72756098
3rd row67,58682927
4th row67,94926829
5th row68,68463415

Common Values

ValueCountFrequency (%)
65,52975611
 
6.2%
66,727560981
 
6.2%
67,586829271
 
6.2%
67,949268291
 
6.2%
68,684634151
 
6.2%
68,841219511
 
6.2%
69,683902441
 
6.2%
70,072195121
 
6.2%
70,578780491
 
6.2%
70,743658541
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:37.758149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
65,52975611
 
6.2%
66,727560981
 
6.2%
67,586829271
 
6.2%
67,949268291
 
6.2%
68,684634151
 
6.2%
68,841219511
 
6.2%
69,683902441
 
6.2%
70,072195121
 
6.2%
70,578780491
 
6.2%
70,743658541
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
621
12.0%
720
11.4%
119
10.9%
417
9.7%
,16
9.1%
216
9.1%
816
9.1%
515
8.6%
915
8.6%
010
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
621
13.2%
720
12.6%
119
11.9%
417
10.7%
216
10.1%
816
10.1%
515
9.4%
915
9.4%
010
6.3%
310
6.3%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
621
12.0%
720
11.4%
119
10.9%
417
9.7%
,16
9.1%
216
9.1%
816
9.1%
515
8.6%
915
8.6%
010
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
621
12.0%
720
11.4%
119
10.9%
417
9.7%
,16
9.1%
216
9.1%
816
9.1%
515
8.6%
915
8.6%
010
5.7%
Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size256.0 B
78,74146341
78,53902439
77,48780488
77,68780488
77,98780488
Other values (9)

Length

Max length11
Median length11
Mean length10.8125
Min length10

Characters and Unicode

Total characters173
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)75.0%

Sample

1st row77,48780488
2nd row77,68780488
3rd row77,98780488
4th row78,03902439
5th row78,3902439

Common Values

ValueCountFrequency (%)
78,741463412
12.5%
78,539024392
12.5%
77,487804881
 
6.2%
77,687804881
 
6.2%
77,987804881
 
6.2%
78,039024391
 
6.2%
78,39024391
 
6.2%
78,541463411
 
6.2%
78,641463411
 
6.2%
78,841463411
 
6.2%
Other values (4)4
25.0%

Length

2022-06-22T15:25:37.846552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78,741463412
12.5%
78,539024392
12.5%
77,487804881
 
6.2%
77,687804881
 
6.2%
77,987804881
 
6.2%
78,039024391
 
6.2%
78,39024391
 
6.2%
78,541463411
 
6.2%
78,641463411
 
6.2%
78,841463411
 
6.2%
Other values (4)4
25.0%

Most occurring characters

ValueCountFrequency (%)
832
18.5%
728
16.2%
427
15.6%
,16
9.2%
316
9.2%
913
7.5%
012
 
6.9%
110
 
5.8%
69
 
5.2%
27
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number157
90.8%
Other Punctuation16
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
832
20.4%
728
17.8%
427
17.2%
316
10.2%
913
8.3%
012
 
7.6%
110
 
6.4%
69
 
5.7%
27
 
4.5%
53
 
1.9%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
832
18.5%
728
16.2%
427
15.6%
,16
9.2%
316
9.2%
913
7.5%
012
 
6.9%
110
 
5.8%
69
 
5.2%
27
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
832
18.5%
728
16.2%
427
15.6%
,16
9.2%
316
9.2%
913
7.5%
012
 
6.9%
110
 
5.8%
69
 
5.2%
27
 
4.0%
Distinct15
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size256.0 B
81,25609756
79,04878049
 
1
79,24878049
 
1
79,44878049
 
1
79,6
 
1
Other values (10)
10 

Length

Max length11
Median length11
Mean length10.5625
Min length4

Characters and Unicode

Total characters169
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)87.5%

Sample

1st row79,04878049
2nd row79,24878049
3rd row79,44878049
4th row79,6
5th row80,05121951

Common Values

ValueCountFrequency (%)
81,256097562
 
12.5%
79,048780491
 
6.2%
79,248780491
 
6.2%
79,448780491
 
6.2%
79,61
 
6.2%
80,051219511
 
6.2%
80,402439021
 
6.2%
80,951219511
 
6.2%
80,904878051
 
6.2%
81,004878051
 
6.2%
Other values (5)5
31.2%

Length

2022-06-22T15:25:37.936800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
81,256097562
 
12.5%
79,048780491
 
6.2%
79,248780491
 
6.2%
79,448780491
 
6.2%
79,61
 
6.2%
80,051219511
 
6.2%
80,402439021
 
6.2%
80,951219511
 
6.2%
80,904878051
 
6.2%
81,004878051
 
6.2%
Other values (5)5
31.2%

Most occurring characters

ValueCountFrequency (%)
028
16.6%
826
15.4%
919
11.2%
,16
9.5%
516
9.5%
715
8.9%
414
8.3%
113
7.7%
210
 
5.9%
69
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number153
90.5%
Other Punctuation16
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028
18.3%
826
17.0%
919
12.4%
516
10.5%
715
9.8%
414
9.2%
113
8.5%
210
 
6.5%
69
 
5.9%
33
 
2.0%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common169
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028
16.6%
826
15.4%
919
11.2%
,16
9.5%
516
9.5%
715
8.9%
414
8.3%
113
7.7%
210
 
5.9%
69
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028
16.6%
826
15.4%
919
11.2%
,16
9.5%
516
9.5%
715
8.9%
414
8.3%
113
7.7%
210
 
5.9%
69
 
5.3%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
71,896
 
1
72,26
 
1
72,618
 
1
72,966
 
1
73,3
 
1
Other values (11)
11 

Length

Max length6
Median length6
Mean length5.75
Min length4

Characters and Unicode

Total characters92
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row71,896
2nd row72,26
3rd row72,618
4th row72,966
5th row73,3

Common Values

ValueCountFrequency (%)
71,8961
 
6.2%
72,261
 
6.2%
72,6181
 
6.2%
72,9661
 
6.2%
73,31
 
6.2%
73,6191
 
6.2%
73,9211
 
6.2%
74,2091
 
6.2%
74,4831
 
6.2%
74,7451
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:38.032209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
71,8961
 
6.2%
72,261
 
6.2%
72,6181
 
6.2%
72,9661
 
6.2%
73,31
 
6.2%
73,6191
 
6.2%
73,9211
 
6.2%
74,2091
 
6.2%
74,4831
 
6.2%
74,7451
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
718
19.6%
,16
17.4%
69
9.8%
49
9.8%
28
8.7%
97
 
7.6%
86
 
6.5%
36
 
6.5%
56
 
6.5%
15
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76
82.6%
Other Punctuation16
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
718
23.7%
69
11.8%
49
11.8%
28
10.5%
97
 
9.2%
86
 
7.9%
36
 
7.9%
56
 
7.9%
15
 
6.6%
02
 
2.6%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common92
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
718
19.6%
,16
17.4%
69
9.8%
49
9.8%
28
8.7%
97
 
7.6%
86
 
6.5%
36
 
6.5%
56
 
6.5%
15
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII92
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
718
19.6%
,16
17.4%
69
9.8%
49
9.8%
28
8.7%
97
 
7.6%
86
 
6.5%
36
 
6.5%
56
 
6.5%
15
 
5.4%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
64,5
 
1
64,918
 
1
65,35
 
1
65,794
 
1
66,244
 
1
Other values (11)
11 

Length

Max length6
Median length6
Mean length5.75
Min length4

Characters and Unicode

Total characters92
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row64,5
2nd row64,918
3rd row65,35
4th row65,794
5th row66,244

Common Values

ValueCountFrequency (%)
64,51
 
6.2%
64,9181
 
6.2%
65,351
 
6.2%
65,7941
 
6.2%
66,2441
 
6.2%
66,6931
 
6.2%
67,131
 
6.2%
67,5451
 
6.2%
67,9311
 
6.2%
68,2861
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:38.129112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
64,51
 
6.2%
64,9181
 
6.2%
65,351
 
6.2%
65,7941
 
6.2%
66,2441
 
6.2%
66,6931
 
6.2%
67,131
 
6.2%
67,5451
 
6.2%
67,9311
 
6.2%
68,2861
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
625
27.2%
,16
17.4%
99
 
9.8%
58
 
8.7%
88
 
8.7%
47
 
7.6%
77
 
7.6%
15
 
5.4%
34
 
4.3%
22
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76
82.6%
Other Punctuation16
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
625
32.9%
99
 
11.8%
58
 
10.5%
88
 
10.5%
47
 
9.2%
77
 
9.2%
15
 
6.6%
34
 
5.3%
22
 
2.6%
01
 
1.3%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common92
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
625
27.2%
,16
17.4%
99
 
9.8%
58
 
8.7%
88
 
8.7%
47
 
7.6%
77
 
7.6%
15
 
5.4%
34
 
4.3%
22
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII92
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
625
27.2%
,16
17.4%
99
 
9.8%
58
 
8.7%
88
 
8.7%
47
 
7.6%
77
 
7.6%
15
 
5.4%
34
 
4.3%
22
 
2.2%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
75,3
 
1
75,296
 
1
75,255
 
1
75,194
 
1
75,128
 
1
Other values (11)
11 

Length

Max length6
Median length6
Mean length5.8125
Min length4

Characters and Unicode

Total characters93
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row75,3
2nd row75,296
3rd row75,255
4th row75,194
5th row75,128

Common Values

ValueCountFrequency (%)
75,31
 
6.2%
75,2961
 
6.2%
75,2551
 
6.2%
75,1941
 
6.2%
75,1281
 
6.2%
75,0651
 
6.2%
75,0111
 
6.2%
74,9661
 
6.2%
74,931
 
6.2%
74,9081
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:38.229977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
75,31
 
6.2%
75,2961
 
6.2%
75,2551
 
6.2%
75,1941
 
6.2%
75,1281
 
6.2%
75,0651
 
6.2%
75,0111
 
6.2%
74,9661
 
6.2%
74,931
 
6.2%
74,9081
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
718
19.4%
,16
17.2%
513
14.0%
411
11.8%
910
10.8%
17
 
7.5%
05
 
5.4%
24
 
4.3%
64
 
4.3%
33
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number77
82.8%
Other Punctuation16
 
17.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
718
23.4%
513
16.9%
411
14.3%
910
13.0%
17
 
9.1%
05
 
6.5%
24
 
5.2%
64
 
5.2%
33
 
3.9%
82
 
2.6%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common93
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
718
19.4%
,16
17.2%
513
14.0%
411
11.8%
910
10.8%
17
 
7.5%
05
 
5.4%
24
 
4.3%
64
 
4.3%
33
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII93
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
718
19.4%
,16
17.2%
513
14.0%
411
11.8%
910
10.8%
17
 
7.5%
05
 
5.4%
24
 
4.3%
64
 
4.3%
33
 
3.2%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
53,447
 
1
53,795
 
1
54,452
 
1
55,36
 
1
56,46
 
1
Other values (11)
11 

Length

Max length6
Median length6
Mean length5.8125
Min length5

Characters and Unicode

Total characters93
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row53,447
2nd row53,795
3rd row54,452
4th row55,36
5th row56,46

Common Values

ValueCountFrequency (%)
53,4471
 
6.2%
53,7951
 
6.2%
54,4521
 
6.2%
55,361
 
6.2%
56,461
 
6.2%
57,6691
 
6.2%
58,8951
 
6.2%
60,061
 
6.2%
61,0991
 
6.2%
61,9681
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:38.325072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
53,4471
 
6.2%
53,7951
 
6.2%
54,4521
 
6.2%
55,361
 
6.2%
56,461
 
6.2%
57,6691
 
6.2%
58,8951
 
6.2%
60,061
 
6.2%
61,0991
 
6.2%
61,9681
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
617
18.3%
,16
17.2%
514
15.1%
310
10.8%
48
8.6%
98
8.6%
75
 
5.4%
85
 
5.4%
15
 
5.4%
03
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number77
82.8%
Other Punctuation16
 
17.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
617
22.1%
514
18.2%
310
13.0%
48
10.4%
98
10.4%
75
 
6.5%
85
 
6.5%
15
 
6.5%
03
 
3.9%
22
 
2.6%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common93
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
617
18.3%
,16
17.2%
514
15.1%
310
10.8%
48
8.6%
98
8.6%
75
 
5.4%
85
 
5.4%
15
 
5.4%
03
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII93
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
617
18.3%
,16
17.2%
514
15.1%
310
10.8%
48
8.6%
98
8.6%
75
 
5.4%
85
 
5.4%
15
 
5.4%
03
 
3.2%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
72,985
 
1
73,271
 
1
73,553
 
1
73,835
 
1
74,119
 
1
Other values (11)
11 

Length

Max length6
Median length6
Mean length5.875
Min length5

Characters and Unicode

Total characters94
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row72,985
2nd row73,271
3rd row73,553
4th row73,835
5th row74,119

Common Values

ValueCountFrequency (%)
72,9851
 
6.2%
73,2711
 
6.2%
73,5531
 
6.2%
73,8351
 
6.2%
74,1191
 
6.2%
74,4091
 
6.2%
74,7081
 
6.2%
75,0131
 
6.2%
75,3211
 
6.2%
75,6291
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:38.411830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
72,9851
 
6.2%
73,2711
 
6.2%
73,5531
 
6.2%
73,8351
 
6.2%
74,1191
 
6.2%
74,4091
 
6.2%
74,7081
 
6.2%
75,0131
 
6.2%
75,3211
 
6.2%
75,6291
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
722
23.4%
,16
17.0%
58
 
8.5%
27
 
7.4%
97
 
7.4%
37
 
7.4%
17
 
7.4%
46
 
6.4%
05
 
5.3%
65
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number78
83.0%
Other Punctuation16
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
722
28.2%
58
 
10.3%
27
 
9.0%
97
 
9.0%
37
 
9.0%
17
 
9.0%
46
 
7.7%
05
 
6.4%
65
 
6.4%
84
 
5.1%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common94
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
722
23.4%
,16
17.0%
58
 
8.5%
27
 
7.4%
97
 
7.4%
37
 
7.4%
17
 
7.4%
46
 
6.4%
05
 
5.3%
65
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII94
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
722
23.4%
,16
17.0%
58
 
8.5%
27
 
7.4%
97
 
7.4%
37
 
7.4%
17
 
7.4%
46
 
6.4%
05
 
5.3%
65
 
5.3%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
68,92021195
 
1
69,26229064
 
1
69,5915489
 
1
69,89951482
 
1
70,24645381
 
1
Other values (11)
11 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters175
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row68,92021195
2nd row69,26229064
3rd row69,5915489
4th row69,89951482
5th row70,24645381

Common Values

ValueCountFrequency (%)
68,920211951
 
6.2%
69,262290641
 
6.2%
69,59154891
 
6.2%
69,899514821
 
6.2%
70,246453811
 
6.2%
70,556690281
 
6.2%
70,884017031
 
6.2%
71,173298991
 
6.2%
71,465864411
 
6.2%
71,746054731
 
6.2%
Other values (6)6
37.5%

Length

2022-06-22T15:25:38.499659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
68,920211951
 
6.2%
69,262290641
 
6.2%
69,59154891
 
6.2%
69,899514821
 
6.2%
70,246453811
 
6.2%
70,556690281
 
6.2%
70,884017031
 
6.2%
71,173298991
 
6.2%
71,465864411
 
6.2%
71,746054731
 
6.2%
Other values (6)6
37.5%

Most occurring characters

ValueCountFrequency (%)
723
13.1%
620
11.4%
218
10.3%
118
10.3%
917
9.7%
,16
9.1%
415
8.6%
014
8.0%
513
7.4%
812
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
90.9%
Other Punctuation16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
723
14.5%
620
12.6%
218
11.3%
118
11.3%
917
10.7%
415
9.4%
014
8.8%
513
8.2%
812
7.5%
39
 
5.7%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
723
13.1%
620
11.4%
218
10.3%
118
10.3%
917
9.7%
,16
9.1%
415
8.6%
014
8.0%
513
7.4%
812
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
723
13.1%
620
11.4%
218
10.3%
118
10.3%
917
9.7%
,16
9.1%
415
8.6%
014
8.0%
513
7.4%
812
6.9%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct11
Distinct (%)100.0%
Missing5
Missing (%)31.2%
Memory size256.0 B
4086,503407
4204,654689
3985,811955
4036,830781
3790,501152
Other values (6)

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st row4086,503407
2nd row4204,654689
3rd row3985,811955
4th row4036,830781
5th row3790,501152

Common Values

ValueCountFrequency (%)
4086,5034071
 
6.2%
4204,6546891
 
6.2%
3985,8119551
 
6.2%
4036,8307811
 
6.2%
3790,5011521
 
6.2%
3997,0794211
 
6.2%
3869,8162291
 
6.2%
3876,9481041
 
6.2%
3939,5295631
 
6.2%
3779,4619211
 
6.2%
(Missing)5
31.2%

Length

2022-06-22T15:25:38.584697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4086,5034071
9.1%
4204,6546891
9.1%
3985,8119551
9.1%
4036,8307811
9.1%
3790,5011521
9.1%
3997,0794211
9.1%
3869,8162291
9.1%
3876,9481041
9.1%
3939,5295631
9.1%
3779,4619211
9.1%

Most occurring characters

ValueCountFrequency (%)
916
13.2%
313
10.7%
412
9.9%
112
9.9%
811
9.1%
,11
9.1%
511
9.1%
010
8.3%
69
7.4%
79
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110
90.9%
Other Punctuation11
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
916
14.5%
313
11.8%
412
10.9%
112
10.9%
811
10.0%
511
10.0%
010
9.1%
69
8.2%
79
8.2%
27
6.4%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
916
13.2%
313
10.7%
412
9.9%
112
9.9%
811
9.1%
,11
9.1%
511
9.1%
010
8.3%
69
7.4%
79
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
916
13.2%
313
10.7%
412
9.9%
112
9.9%
811
9.1%
,11
9.1%
511
9.1%
010
8.3%
69
7.4%
79
7.4%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct11
Distinct (%)100.0%
Missing5
Missing (%)31.2%
Memory size256.0 B
4287,157074
4188,843259
4115,52724
4110,585978
3913,457942
Other values (6)

Length

Max length11
Median length11
Mean length10.72727273
Min length9

Characters and Unicode

Total characters118
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st row4287,157074
2nd row4188,843259
3rd row4115,52724
4th row4110,585978
5th row3913,457942

Common Values

ValueCountFrequency (%)
4287,1570741
 
6.2%
4188,8432591
 
6.2%
4115,527241
 
6.2%
4110,5859781
 
6.2%
3913,4579421
 
6.2%
4016,8480711
 
6.2%
3847,07221
 
6.2%
3836,6562991
 
6.2%
3833,5342591
 
6.2%
3659,0877951
 
6.2%
(Missing)5
31.2%

Length

2022-06-22T15:25:38.674336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4287,1570741
9.1%
4188,8432591
9.1%
4115,527241
9.1%
4110,5859781
9.1%
3913,4579421
9.1%
4016,8480711
9.1%
3847,07221
9.1%
3836,6562991
9.1%
3833,5342591
9.1%
3659,0877951
9.1%

Most occurring characters

ValueCountFrequency (%)
314
11.9%
413
11.0%
813
11.0%
712
10.2%
512
10.2%
,11
9.3%
210
8.5%
110
8.5%
910
8.5%
07
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number107
90.7%
Other Punctuation11
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
314
13.1%
413
12.1%
813
12.1%
712
11.2%
512
11.2%
210
9.3%
110
9.3%
910
9.3%
07
6.5%
66
5.6%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
314
11.9%
413
11.0%
813
11.0%
712
10.2%
512
10.2%
,11
9.3%
210
8.5%
110
8.5%
910
8.5%
07
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
314
11.9%
413
11.0%
813
11.0%
712
10.2%
512
10.2%
,11
9.3%
210
8.5%
110
8.5%
910
8.5%
07
5.9%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct11
Distinct (%)100.0%
Missing5
Missing (%)31.2%
Memory size256.0 B
3214,674121
3175,799871
3149,576553
3087,566331
2869,920712
Other values (6)

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st row3214,674121
2nd row3175,799871
3rd row3149,576553
4th row3087,566331
5th row2869,920712

Common Values

ValueCountFrequency (%)
3214,6741211
 
6.2%
3175,7998711
 
6.2%
3149,5765531
 
6.2%
3087,5663311
 
6.2%
2869,9207121
 
6.2%
2930,5885241
 
6.2%
2828,4048911
 
6.2%
2709,2977281
 
6.2%
2579,4725431
 
6.2%
2414,4840021
 
6.2%
(Missing)5
31.2%

Length

2022-06-22T15:25:38.764999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3214,6741211
9.1%
3175,7998711
9.1%
3149,5765531
9.1%
3087,5663311
9.1%
2869,9207121
9.1%
2930,5885241
9.1%
2828,4048911
9.1%
2709,2977281
9.1%
2579,4725431
9.1%
2414,4840021
9.1%

Most occurring characters

ValueCountFrequency (%)
217
14.0%
415
12.4%
713
10.7%
111
9.1%
,11
9.1%
511
9.1%
811
9.1%
910
8.3%
39
7.4%
07
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110
90.9%
Other Punctuation11
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
217
15.5%
415
13.6%
713
11.8%
111
10.0%
511
10.0%
811
10.0%
910
9.1%
39
8.2%
07
6.4%
66
 
5.5%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
217
14.0%
415
12.4%
713
10.7%
111
9.1%
,11
9.1%
511
9.1%
811
9.1%
910
8.3%
39
7.4%
07
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
217
14.0%
415
12.4%
713
10.7%
111
9.1%
,11
9.1%
511
9.1%
811
9.1%
910
8.3%
39
7.4%
07
5.8%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct11
Distinct (%)100.0%
Missing5
Missing (%)31.2%
Memory size256.0 B
4062,979049
4053,883437
4012,654245
3858,434521
3678,511133
Other values (6)

Length

Max length11
Median length11
Mean length10.90909091
Min length10

Characters and Unicode

Total characters120
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st row4062,979049
2nd row4053,883437
3rd row4012,654245
4th row3858,434521
5th row3678,511133

Common Values

ValueCountFrequency (%)
4062,9790491
 
6.2%
4053,8834371
 
6.2%
4012,6542451
 
6.2%
3858,4345211
 
6.2%
3678,5111331
 
6.2%
3893,2666041
 
6.2%
3610,8121691
 
6.2%
3537,363171
 
6.2%
3567,6293541
 
6.2%
3470,7631291
 
6.2%
(Missing)5
31.2%

Length

2022-06-22T15:25:38.851664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4062,9790491
9.1%
4053,8834371
9.1%
4012,6542451
9.1%
3858,4345211
9.1%
3678,5111331
9.1%
3893,2666041
9.1%
3610,8121691
9.1%
3537,363171
9.1%
3567,6293541
9.1%
3470,7631291
9.1%

Most occurring characters

ValueCountFrequency (%)
320
16.7%
413
10.8%
613
10.8%
,11
9.2%
511
9.2%
111
9.2%
210
8.3%
79
7.5%
88
 
6.7%
07
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number109
90.8%
Other Punctuation11
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
320
18.3%
413
11.9%
613
11.9%
511
10.1%
111
10.1%
210
9.2%
79
8.3%
88
 
7.3%
07
 
6.4%
97
 
6.4%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
320
16.7%
413
10.8%
613
10.8%
,11
9.2%
511
9.2%
111
9.2%
210
8.3%
79
7.5%
88
 
6.7%
07
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
320
16.7%
413
10.8%
613
10.8%
,11
9.2%
511
9.2%
111
9.2%
210
8.3%
79
7.5%
88
 
6.7%
07
 
5.8%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct11
Distinct (%)100.0%
Missing5
Missing (%)31.2%
Memory size256.0 B
8422,034308
8239,946371
8213,389543
8194,880771
7797,121136
Other values (6)

Length

Max length11
Median length11
Mean length10.90909091
Min length10

Characters and Unicode

Total characters120
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st row8422,034308
2nd row8239,946371
3rd row8213,389543
4th row8194,880771
5th row7797,121136

Common Values

ValueCountFrequency (%)
8422,0343081
 
6.2%
8239,9463711
 
6.2%
8213,3895431
 
6.2%
8194,8807711
 
6.2%
7797,1211361
 
6.2%
7788,5607861
 
6.2%
7911,5545881
 
6.2%
7733,4116551
 
6.2%
7743,7257421
 
6.2%
7897,8556151
 
6.2%
(Missing)5
31.2%

Length

2022-06-22T15:25:38.939311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8422,0343081
9.1%
8239,9463711
9.1%
8213,3895431
9.1%
8194,8807711
9.1%
7797,1211361
9.1%
7788,5607861
9.1%
7911,5545881
9.1%
7733,4116551
9.1%
7743,7257421
9.1%
7897,8556151
9.1%

Most occurring characters

ValueCountFrequency (%)
720
16.7%
815
12.5%
114
11.7%
313
10.8%
512
10.0%
,11
9.2%
410
8.3%
27
 
5.8%
97
 
5.8%
67
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number109
90.8%
Other Punctuation11
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
720
18.3%
815
13.8%
114
12.8%
313
11.9%
512
11.0%
410
9.2%
27
 
6.4%
97
 
6.4%
67
 
6.4%
04
 
3.7%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
720
16.7%
815
12.5%
114
11.7%
313
10.8%
512
10.0%
,11
9.2%
410
8.3%
27
 
5.8%
97
 
5.8%
67
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
720
16.7%
815
12.5%
114
11.7%
313
10.8%
512
10.0%
,11
9.2%
410
8.3%
27
 
5.8%
97
 
5.8%
67
 
5.8%
Distinct10
Distinct (%)100.0%
Missing6
Missing (%)37.5%
Memory size256.0 B
4540,908156
4688,391023
4709,844887
4823,125995
4531,286463
Other values (5)

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters110
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row4540,908156
2nd row4688,391023
3rd row4709,844887
4th row4823,125995
5th row4531,286463

Common Values

ValueCountFrequency (%)
4540,9081561
 
6.2%
4688,3910231
 
6.2%
4709,8448871
 
6.2%
4823,1259951
 
6.2%
4531,2864631
 
6.2%
4819,0407821
 
6.2%
5049,4266311
 
6.2%
5167,0103531
 
6.2%
5078,6263421
 
6.2%
4942,8754831
 
6.2%
(Missing)6
37.5%

Length

2022-06-22T15:25:39.026060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T15:25:39.149660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4540,9081561
10.0%
4688,3910231
10.0%
4709,8448871
10.0%
4823,1259951
10.0%
4531,2864631
10.0%
4819,0407821
10.0%
5049,4266311
10.0%
5167,0103531
10.0%
5078,6263421
10.0%
4942,8754831
10.0%

Most occurring characters

ValueCountFrequency (%)
417
15.5%
813
11.8%
510
9.1%
010
9.1%
,10
9.1%
310
9.1%
69
8.2%
29
8.2%
98
7.3%
18
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100
90.9%
Other Punctuation10
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
417
17.0%
813
13.0%
510
10.0%
010
10.0%
310
10.0%
69
9.0%
29
9.0%
98
8.0%
18
8.0%
76
 
6.0%
Other Punctuation
ValueCountFrequency (%)
,10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
417
15.5%
813
11.8%
510
9.1%
010
9.1%
,10
9.1%
310
9.1%
69
8.2%
29
8.2%
98
7.3%
18
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
417
15.5%
813
11.8%
510
9.1%
010
9.1%
,10
9.1%
310
9.1%
69
8.2%
29
8.2%
98
7.3%
18
7.3%
Distinct11
Distinct (%)100.0%
Missing5
Missing (%)31.2%
Memory size256.0 B
7846,499688
7697,652535
7758,165986
7488,081921
7056,783653
Other values (6)

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st row7846,499688
2nd row7697,652535
3rd row7758,165986
4th row7488,081921
5th row7056,783653

Common Values

ValueCountFrequency (%)
7846,4996881
 
6.2%
7697,6525351
 
6.2%
7758,1659861
 
6.2%
7488,0819211
 
6.2%
7056,7836531
 
6.2%
7161,4265521
 
6.2%
7029,9546011
 
6.2%
6872,0272841
 
6.2%
6905,5986331
 
6.2%
6960,6839971
 
6.2%
(Missing)5
31.2%

Length

2022-06-22T15:25:39.249125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7846,4996881
9.1%
7697,6525351
9.1%
7758,1659861
9.1%
7488,0819211
9.1%
7056,7836531
9.1%
7161,4265521
9.1%
7029,9546011
9.1%
6872,0272841
9.1%
6905,5986331
9.1%
6960,6839971
9.1%

Most occurring characters

ValueCountFrequency (%)
620
16.5%
714
11.6%
814
11.6%
914
11.6%
512
9.9%
,11
9.1%
09
7.4%
28
 
6.6%
37
 
5.8%
46
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110
90.9%
Other Punctuation11
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
620
18.2%
714
12.7%
814
12.7%
914
12.7%
512
10.9%
09
8.2%
28
 
7.3%
37
 
6.4%
46
 
5.5%
16
 
5.5%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
620
16.5%
714
11.6%
814
11.6%
914
11.6%
512
9.9%
,11
9.1%
09
7.4%
28
 
6.6%
37
 
5.8%
46
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
620
16.5%
714
11.6%
814
11.6%
914
11.6%
512
9.9%
,11
9.1%
09
7.4%
28
 
6.6%
37
 
5.8%
46
 
5.0%
Distinct11
Distinct (%)100.0%
Missing5
Missing (%)31.2%
Memory size256.0 B
3686,359905
3598,810406
3441,640219
3361,980517
3145,585662
Other values (6)

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st row3686,359905
2nd row3598,810406
3rd row3441,640219
4th row3361,980517
5th row3145,585662

Common Values

ValueCountFrequency (%)
3686,3599051
 
6.2%
3598,8104061
 
6.2%
3441,6402191
 
6.2%
3361,9805171
 
6.2%
3145,5856621
 
6.2%
3230,6159841
 
6.2%
2972,1530651
 
6.2%
3042,8598711
 
6.2%
2987,7005891
 
6.2%
2777,3109871
 
6.2%
(Missing)5
31.2%

Length

2022-06-22T15:25:39.333462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3686,3599051
9.1%
3598,8104061
9.1%
3441,6402191
9.1%
3361,9805171
9.1%
3145,5856621
9.1%
3230,6159841
9.1%
2972,1530651
9.1%
3042,8598711
9.1%
2987,7005891
9.1%
2777,3109871
9.1%

Most occurring characters

ValueCountFrequency (%)
513
10.7%
312
9.9%
612
9.9%
112
9.9%
811
9.1%
,11
9.1%
911
9.1%
011
9.1%
711
9.1%
29
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110
90.9%
Other Punctuation11
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
513
11.8%
312
10.9%
612
10.9%
112
10.9%
811
10.0%
911
10.0%
011
10.0%
711
10.0%
29
8.2%
48
7.3%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
513
10.7%
312
9.9%
612
9.9%
112
9.9%
811
9.1%
,11
9.1%
911
9.1%
011
9.1%
711
9.1%
29
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
513
10.7%
312
9.9%
612
9.9%
112
9.9%
811
9.1%
,11
9.1%
911
9.1%
011
9.1%
711
9.1%
29
7.4%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct10
Distinct (%)100.0%
Missing6
Missing (%)37.5%
Memory size256.0 B
1156,907537
1184,145121
1238,411628
1294,480833
1240,177304
Other values (5)

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters110
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row1156,907537
2nd row1184,145121
3rd row1238,411628
4th row1294,480833
5th row1240,177304

Common Values

ValueCountFrequency (%)
1156,9075371
 
6.2%
1184,1451211
 
6.2%
1238,4116281
 
6.2%
1294,4808331
 
6.2%
1240,1773041
 
6.2%
1358,5024021
 
6.2%
1367,1880491
 
6.2%
1413,7333851
 
6.2%
1461,0767741
 
6.2%
1495,5411411
 
6.2%
(Missing)6
37.5%

Length

2022-06-22T15:25:39.417814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T15:25:39.526826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1156,9075371
10.0%
1184,1451211
10.0%
1238,4116281
10.0%
1294,4808331
10.0%
1240,1773041
10.0%
1358,5024021
10.0%
1367,1880491
10.0%
1413,7333851
10.0%
1461,0767741
10.0%
1495,5411411
10.0%

Most occurring characters

ValueCountFrequency (%)
124
21.8%
415
13.6%
311
10.0%
,10
9.1%
79
 
8.2%
89
 
8.2%
58
 
7.3%
08
 
7.3%
27
 
6.4%
65
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100
90.9%
Other Punctuation10
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
124
24.0%
415
15.0%
311
11.0%
79
 
9.0%
89
 
9.0%
58
 
8.0%
08
 
8.0%
27
 
7.0%
65
 
5.0%
94
 
4.0%
Other Punctuation
ValueCountFrequency (%)
,10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
124
21.8%
415
13.6%
311
10.0%
,10
9.1%
79
 
8.2%
89
 
8.2%
58
 
7.3%
08
 
7.3%
27
 
6.4%
65
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124
21.8%
415
13.6%
311
10.0%
,10
9.1%
79
 
8.2%
89
 
8.2%
58
 
7.3%
08
 
7.3%
27
 
6.4%
65
 
4.5%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct10
Distinct (%)100.0%
Missing6
Missing (%)37.5%
Memory size256.0 B
449,765857
466,1382611
485,0996287
501,5596362
544,6265973
Other values (5)

Length

Max length11
Median length11
Mean length10.8
Min length10

Characters and Unicode

Total characters108
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row449,765857
2nd row466,1382611
3rd row485,0996287
4th row501,5596362
5th row544,6265973

Common Values

ValueCountFrequency (%)
449,7658571
 
6.2%
466,13826111
 
6.2%
485,09962871
 
6.2%
501,55963621
 
6.2%
544,62659731
 
6.2%
561,65340591
 
6.2%
577,99442631
 
6.2%
599,15561981
 
6.2%
605,79403781
 
6.2%
636,5718341
 
6.2%
(Missing)6
37.5%

Length

2022-06-22T15:25:39.631890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T15:25:39.741736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
449,7658571
10.0%
466,13826111
10.0%
485,09962871
10.0%
501,55963621
10.0%
544,62659731
10.0%
561,65340591
10.0%
577,99442631
10.0%
599,15561981
10.0%
605,79403781
10.0%
636,5718341
10.0%

Most occurring characters

ValueCountFrequency (%)
517
15.7%
616
14.8%
912
11.1%
411
10.2%
,10
9.3%
79
8.3%
18
7.4%
38
7.4%
87
6.5%
25
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number98
90.7%
Other Punctuation10
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
517
17.3%
616
16.3%
912
12.2%
411
11.2%
79
9.2%
18
8.2%
38
8.2%
87
7.1%
25
 
5.1%
05
 
5.1%
Other Punctuation
ValueCountFrequency (%)
,10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
517
15.7%
616
14.8%
912
11.1%
411
10.2%
,10
9.3%
79
8.3%
18
7.4%
38
7.4%
87
6.5%
25
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
517
15.7%
616
14.8%
912
11.1%
411
10.2%
,10
9.3%
79
8.3%
18
7.4%
38
7.4%
87
6.5%
25
 
4.6%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct11
Distinct (%)100.0%
Missing5
Missing (%)31.2%
Memory size256.0 B
1685,099737
1698,585327
1662,60258
1621,264964
1599,516711
Other values (6)

Length

Max length11
Median length11
Mean length10.90909091
Min length10

Characters and Unicode

Total characters120
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st row1685,099737
2nd row1698,585327
3rd row1662,60258
4th row1621,264964
5th row1599,516711

Common Values

ValueCountFrequency (%)
1685,0997371
 
6.2%
1698,5853271
 
6.2%
1662,602581
 
6.2%
1621,2649641
 
6.2%
1599,5167111
 
6.2%
1531,7569681
 
6.2%
1587,0720711
 
6.2%
1634,6974181
 
6.2%
1616,6136451
 
6.2%
1561,8733421
 
6.2%
(Missing)5
31.2%

Length

2022-06-22T15:25:39.846218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1685,0997371
9.1%
1698,5853271
9.1%
1662,602581
9.1%
1621,2649641
9.1%
1599,5167111
9.1%
1531,7569681
9.1%
1587,0720711
9.1%
1634,6974181
9.1%
1616,6136451
9.1%
1561,8733421
9.1%

Most occurring characters

ValueCountFrequency (%)
123
19.2%
619
15.8%
512
10.0%
,11
9.2%
711
9.2%
29
 
7.5%
88
 
6.7%
98
 
6.7%
38
 
6.7%
47
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number109
90.8%
Other Punctuation11
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123
21.1%
619
17.4%
512
11.0%
711
10.1%
29
 
8.3%
88
 
7.3%
98
 
7.3%
38
 
7.3%
47
 
6.4%
04
 
3.7%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
123
19.2%
619
15.8%
512
10.0%
,11
9.2%
711
9.2%
29
 
7.5%
88
 
6.7%
98
 
6.7%
38
 
6.7%
47
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
19.2%
619
15.8%
512
10.0%
,11
9.2%
711
9.2%
29
 
7.5%
88
 
6.7%
98
 
6.7%
38
 
6.7%
47
 
5.8%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct10
Distinct (%)100.0%
Missing6
Missing (%)37.5%
Memory size256.0 B
2678,554224
2625,794791
2775,615258
2950,15361
2852,095448
Other values (5)

Length

Max length11
Median length11
Mean length10.9
Min length10

Characters and Unicode

Total characters109
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row2678,554224
2nd row2625,794791
3rd row2775,615258
4th row2950,15361
5th row2852,095448

Common Values

ValueCountFrequency (%)
2678,5542241
 
6.2%
2625,7947911
 
6.2%
2775,6152581
 
6.2%
2950,153611
 
6.2%
2852,0954481
 
6.2%
2768,0945071
 
6.2%
2716,6811731
 
6.2%
2636,6847261
 
6.2%
2602,8455981
 
6.2%
2695,5057761
 
6.2%
(Missing)6
37.5%

Length

2022-06-22T15:25:39.941028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T15:25:40.053204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2678,5542241
10.0%
2625,7947911
10.0%
2775,6152581
10.0%
2950,153611
10.0%
2852,0954481
10.0%
2768,0945071
10.0%
2716,6811731
10.0%
2636,6847261
10.0%
2602,8455981
10.0%
2695,5057761
10.0%

Most occurring characters

ValueCountFrequency (%)
217
15.6%
516
14.7%
614
12.8%
712
11.0%
,10
9.2%
89
8.3%
48
7.3%
97
6.4%
17
6.4%
06
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99
90.8%
Other Punctuation10
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
217
17.2%
516
16.2%
614
14.1%
712
12.1%
89
9.1%
48
8.1%
97
7.1%
17
7.1%
06
 
6.1%
33
 
3.0%
Other Punctuation
ValueCountFrequency (%)
,10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common109
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
217
15.6%
516
14.7%
614
12.8%
712
11.0%
,10
9.2%
89
8.3%
48
7.3%
97
6.4%
17
6.4%
06
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
217
15.6%
516
14.7%
614
12.8%
712
11.0%
,10
9.2%
89
8.3%
48
7.3%
97
6.4%
17
6.4%
06
 
5.5%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct10
Distinct (%)100.0%
Missing6
Missing (%)37.5%
Memory size256.0 B
1393,691324
1515,173678
1630,171029
1672,90412
1778,433519
Other values (5)

Length

Max length11
Median length11
Mean length10.9
Min length10

Characters and Unicode

Total characters109
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row1393,691324
2nd row1515,173678
3rd row1630,171029
4th row1672,90412
5th row1778,433519

Common Values

ValueCountFrequency (%)
1393,6913241
 
6.2%
1515,1736781
 
6.2%
1630,1710291
 
6.2%
1672,904121
 
6.2%
1778,4335191
 
6.2%
1954,7225561
 
6.2%
2085,0830221
 
6.2%
2149,6025691
 
6.2%
2204,2432991
 
6.2%
2224,3548981
 
6.2%
(Missing)6
37.5%

Length

2022-06-22T15:25:40.161022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T15:25:40.291084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1393,6913241
10.0%
1515,1736781
10.0%
1630,1710291
10.0%
1672,904121
10.0%
1778,4335191
10.0%
1954,7225561
10.0%
2085,0830221
10.0%
2149,6025691
10.0%
2204,2432991
10.0%
2224,3548981
10.0%

Most occurring characters

ValueCountFrequency (%)
218
16.5%
114
12.8%
911
10.1%
310
9.2%
,10
9.2%
49
8.3%
59
8.3%
08
7.3%
67
 
6.4%
77
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99
90.8%
Other Punctuation10
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
218
18.2%
114
14.1%
911
11.1%
310
10.1%
49
9.1%
59
9.1%
08
8.1%
67
 
7.1%
77
 
7.1%
86
 
6.1%
Other Punctuation
ValueCountFrequency (%)
,10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common109
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
218
16.5%
114
12.8%
911
10.1%
310
9.2%
,10
9.2%
49
8.3%
59
8.3%
08
7.3%
67
 
6.4%
77
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
218
16.5%
114
12.8%
911
10.1%
310
9.2%
,10
9.2%
49
8.3%
59
8.3%
08
7.3%
67
 
6.4%
77
 
6.4%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct10
Distinct (%)100.0%
Missing6
Missing (%)37.5%
Memory size256.0 B
1767,06317
1796,644789
1824,035298
1829,629201
1796,215452
Other values (5)

Length

Max length11
Median length11
Mean length10.9
Min length10

Characters and Unicode

Total characters109
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row1767,06317
2nd row1796,644789
3rd row1824,035298
4th row1829,629201
5th row1796,215452

Common Values

ValueCountFrequency (%)
1767,063171
 
6.2%
1796,6447891
 
6.2%
1824,0352981
 
6.2%
1829,6292011
 
6.2%
1796,2154521
 
6.2%
1874,6576881
 
6.2%
1881,4775481
 
6.2%
1891,7004261
 
6.2%
1894,1120591
 
6.2%
1919,9917651
 
6.2%
(Missing)6
37.5%

Length

2022-06-22T15:25:40.388820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T15:25:40.495171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1767,063171
10.0%
1796,6447891
10.0%
1824,0352981
10.0%
1829,6292011
10.0%
1796,2154521
10.0%
1874,6576881
10.0%
1881,4775481
10.0%
1891,7004261
10.0%
1894,1120591
10.0%
1919,9917651
10.0%

Most occurring characters

ValueCountFrequency (%)
119
17.4%
913
11.9%
712
11.0%
812
11.0%
610
9.2%
,10
9.2%
49
8.3%
29
8.3%
57
 
6.4%
06
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99
90.8%
Other Punctuation10
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
119
19.2%
913
13.1%
712
12.1%
812
12.1%
610
10.1%
49
9.1%
29
9.1%
57
 
7.1%
06
 
6.1%
32
 
2.0%
Other Punctuation
ValueCountFrequency (%)
,10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common109
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
119
17.4%
913
11.9%
712
11.0%
812
11.0%
610
9.2%
,10
9.2%
49
8.3%
29
8.3%
57
 
6.4%
06
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
119
17.4%
913
11.9%
712
11.0%
812
11.0%
610
9.2%
,10
9.2%
49
8.3%
29
8.3%
57
 
6.4%
06
 
5.5%

Interactions

2022-06-22T15:25:23.399353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:01.877818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:03.426142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:04.821897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:05.940663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:07.275624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:08.550159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:09.790671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:11.218552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:12.611615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:14.011067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:15.610739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:16.866403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:18.150871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:19.625230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:20.893057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:22.110735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:23.471247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:01.981254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:03.497788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:04.879445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:06.002445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:07.342952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:08.614595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:09.855394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:11.287197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:12.689819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:14.084239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:15.676215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:16.933628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:18.214574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:19.695852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:20.959574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:22.180450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:23.549237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:02.087072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:03.574963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:04.942420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:06.072441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:07.417619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:08.685460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:09.929647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:11.361590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:12.782600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:14.164759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:15.747398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:17.004387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:18.282746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:19.767057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:21.030131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:22.257304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:23.621450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:02.182555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:03.646679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:05.001605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:06.140049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:07.486379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:08.754896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:09.999635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:11.442941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:12.860844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:14.240865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:15.816427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:17.073473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:18.346351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:19.834321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:21.096608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:22.328229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:23.697563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:02.267558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:03.723178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:05.066382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:06.209810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:07.559477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:08.829522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:10.075077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:11.549995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:12.942754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:14.324016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:15.891133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:17.153124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:18.415561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:19.909132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:21.169553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:22.403035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:23.772510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:02.347722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:03.802959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:05.132570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:06.280135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:07.631747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:08.904104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:10.150141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:11.632210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:13.025439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:14.616810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:15.964204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:17.233990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:18.487992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:19.985597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:21.243664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:22.477941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:23.846582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:02.540389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:03.879760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:05.197819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:06.349065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:07.705816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:08.977860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:10.387836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:02.614178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:03.954651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:05.261247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:06.416637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:07.777293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:09.047850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:10.462835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:11.796139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:13.185378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:14.781682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:16.121222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:17.383016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:18.629871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:20.130502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:21.386082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:22.628345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:23.997582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:02.702546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:04.035971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:05.328933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:06.487953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:09.121921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:21.462510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:24.077361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:02.791812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:04.115938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:05.397976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:06.563152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:07.934258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:17.611691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:21.618549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:22.869930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:24.229059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:02.953703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:04.270713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:20.522813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:21.765545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:06.850358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:24.455154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:24.609708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:04.643748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:11.142631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:12.524594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:13.925451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:16.791015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-22T15:25:22.037463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T15:25:23.316554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-22T15:25:40.604874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-22T15:25:40.897077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-22T15:25:41.167090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-22T15:25:41.553699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-22T15:25:43.542120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-22T15:25:25.511185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-22T15:25:27.016039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-22T15:25:27.355079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

yeardisputenon_violent_crisesviolent_criseslimited_warswarstotalGDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD]GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU]GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA]GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA]GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN]GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN]GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS]GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA]GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR]GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA]GDP (current US$) [NY.GDP.MKTP.CD] - India [IND]GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX]GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF]GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN]GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD]Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU]Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA]Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA]Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN]Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN]Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS]Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA]Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR]Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA]Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND]Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX]Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF]Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN]Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Germany [DEU]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - France [FRA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Italy [ITA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD]
01970-01-01 00:00:00.0000020056393902622740,7317071641,663219980,8178489741,8039008724,9958608696,3999654483,5132137992,593280013,2021320627,9234306212,3078070665,27705197311,394591814,0481740082,84686E+122,19695E+121,85822E+124,83147E+121,17311E+127,64017E+111,30366E+132,54483E+128,91634E+118,20382E+118,77476E+112,88868E+112,28597E+124,77779E+131,0656297012,0237069191,6051704160,9315816531,1106696553,3301031924,0900348762,4342032281,5240133932,7549076660,3559589311,383769691,853342442,3944310873032496517944442050523297376423924430061333012988132964273369772745,33203E+1161653635038135886197362307231292531234549783566963815427899536511,15955E+128,312,89,68,4110,610,2141217,24324,08721,74123,07312,420,5519305178,9317073280,1634146380,7829268381,9251219580,1926829365,529756177,4878048879,0487804971,89664,575,353,44772,98568,920211954086,5034074287,1570743214,6741214062,9790498422,0343084540,9081567846,4996883686,3599051156,907537449,7658571685,0997372678,5542241393,6913241767,06317
11970-01-01 00:00:00.000002006721141043063263,8164419132,4493236011,7906396811,3723501284,1658176278,2000682552,8549722942,5841046773,9619887098,0607325734,4950778945,60380645912,720955674,4956256642,9947E+122,32054E+121,94955E+124,60166E+121,31926E+129,89931E+111,38146E+132,71706E+121,10763E+129,4026E+119,75387E+113,03861E+112,75213E+125,17787E+131,1993029581,97509831,5254829050,9171970381,1258324083,2452543794,0416272372,3730869991,4810848952,526808630,3111719361,2907469831,854648842,3641655773588365797345792148416296330222634155259288614809892803345177816195,58335E+1164217619528164048673072395192795830351310193506139658514533732341,20604E+128,213,19,68,6510,910,314,312,316,74723,56421,34323,23812,0920,3977565579,1317073280,8121951281,2829268382,3219512280,3439024466,7275609877,6878048879,2487804972,2664,91875,29653,79573,27169,262290644204,6546894188,8432593175,7998714053,8834378239,9463714688,3910237697,6525353598,8104061184,145121466,13826111698,5853272625,7947911515,1736781796,644789
21970-01-01 00:00:00.000002007791261072663442,9764551312,4247362431,487072981,4839694126,8686088578,4999777681,8761714542,2694868756,0698706077,6608150652,2914457145,36047405414,230860934,4387543073,42558E+122,66059E+122,2131E+124,57975E+121,46882E+121,29971E+121,44519E+133,10618E+121,39711E+121,21674E+121,0527E+123,33075E+113,55034E+125,83375E+131,1724717651,9075286341,4470525190,8976266541,1889017833,1185420794,0796550812,3742077481,46629212,4782482090,4011639181,1775172531,7398128592,3284628714011085927650684467384319824317924053004568817417139931435349949965,89586E+1173448032014204857580152825477345042230376463525684244621365907551,33768E+128,312,89,78,6311,211,314,312,616,30622,99620,97323,36712,120,3071010579,5341463481,1121951281,4341463482,5070731780,5439024467,5868292777,9878048879,4487804972,61865,3575,25554,45273,55369,59154893985,8119554115,527243149,5765534012,6542458213,3895434709,8448877758,1659863441,6402191238,411628485,09962871662,602582775,6152581630,1710291824,035298
31970-01-01 00:00:00.000002008821301023093530,9598791340,25494596-0,962012841-1,2242890011,0076226955,199969265-0,136579803-0,2396380855,0941954483,086698061,1435845873,1910438869,650678922,0008305123,74526E+122,9303E+122,40866E+125,10668E+121,55299E+121,66085E+121,47128E+132,93888E+121,69586E+121,1989E+121,10999E+123,16132E+114,59431E+126,40715E+131,2090340041,8970758961,5356749840,9202478431,2486213823,1494860174,4638273562,4957230161,4419240932,6314621410,3905132271,1458433141,7123343612,3864247064509895631255365965845368399897464636146828019342058405561837853936,56756E+1172915407670244529030363300237672743346541243285925081788408028201,50972E+128,312,99,88,711,4121412,915,92122,3920,63523,43312,1420,1962589679,7365853781,2146341581,4853658582,5875609880,6951219567,9492682978,0390243979,672,96665,79475,19455,3673,83569,899514824036,8307814110,5859783087,5663313858,4345218194,8807714823,1259957488,0819213361,9805171294,480833501,55963621621,2649642950,153611672,904121829,629201
41970-01-01 00:00:00.000002009107118110258368-5,693836336-2,873313828-5,280937208-5,693236359-2,928400167-7,799993913-2,536757067-4,247356266-0,1258120037,861888833-5,285744137-1,5380891359,398725632-1,3072573,41126E+122,70089E+122,19993E+125,28949E+121,37463E+121,22264E+121,44489E+132,4258E+121,667E+121,34189E+129,00045E+113,29753E+115,1017E+126,0781E+131,3106040732,0981473351,5542107020,9837773571,3775556313,9240633994,885599682,6534850161,5386256973,1293871790,5015562751,2140020431,8861331522,6179042874452892783656441455398340544813245146515820818936226052515321167987,05917E+1164010506670256488099113872215439245142339143592687702966016667531,56427E+128,112,89,68,511,312,313,512,715,59521,75520,32723,41711,9519,9678514379,8365853781,4146341581,6365853782,9314634180,9951219568,6846341578,390243980,0512195173,366,24475,12856,4674,11970,246453813790,5011523913,4579422869,9207123678,5111337797,1211364531,2864637056,7836533145,5856621240,177304544,62659731599,5167112852,0954481778,4335191796,215452
51970-01-01 00:00:00.000002010951081392263704,1798824991,9494376231,7132958394,0979179193,089494624,52,563766562,1314381987,5282258188,4975847025,1181181433,03973288110,635871064,4946945693,39967E+122,64519E+122,1361E+125,75907E+121,61734E+121,52492E+121,49921E+132,49111E+122,20884E+121,67562E+121,0578E+124,17365E+116,08716E+126,65005E+131,2668266261,9694193531,5004945760,9588511331,1943383383,5850857214,9226416772,5781595021,5394069812,8894613280,4527344931,1158082511,739475152,5229104954302591501252044060565320208199515465545073519315688825587202276097,38005E+11639791119703400294447046090445657478903133941881680921,05523E+111,6484E+128,312,99,58,511,112,51312,915,32721,11420,04223,30511,919,7787011779,9878048881,6634146382,0365853782,8426829381,2463414668,8412195178,5414634180,4024390273,61966,69375,06557,66974,40970,556690283997,0794214016,8480712930,5885243893,2666047788,5607864819,0407827161,4265523230,6159841358,502402561,65340591531,7569682768,0945071954,7225561874,657688
61970-01-01 00:00:00.0000020111068715519203873,9251927052,1927006330,7073333470,0238095243,1468813724,3000291861,5508355051,4575633913,9744230795,2413150013,663007933,1685562799,5508321793,3396178863,74931E+122,86516E+122,29499E+126,23315E+121,79333E+122,04593E+121,55426E+132,67489E+122,61616E+121,82305E+121,18049E+124,58202E+117,5515E+127,36715E+131,2061503361,8914062141,4759565970,9868064931,1932918953,4330438384,8401739952,5027264021,4118511592,7044835580,4657778031,1032576761,6655760532,4120333844516321287854120871010338288049716076221384121393720864702375239517,52288E+11665695525733693620989649633815794549845854245941540781,25286E+111,75048E+128,312,79,28,31112,612,712,815,1120,49519,76623,09713,2719,8546705380,4365853782,1146341582,1878048882,5912195181,4487804969,6839024478,6414634180,9512195173,92167,1375,01158,89574,70870,884017033869,8162293847,07222828,4048913610,8121697911,5545885049,4266317029,9546012972,1530651367,188049577,99442631587,0720712716,6811732085,0830221881,477548
71970-01-01 00:00:00.000002012998517725194050,4184975940,313134751-2,9809057681,3747509991,7622225494,0240861572,2495458521,4698875211,9211759855,4563887533,6423226792,3962323857,8637364492,6728184113,52714E+122,68367E+122,08696E+126,27236E+121,82837E+122,2083E+121,6197E+132,71916E+122,46523E+121,82764E+121,20109E+124,34401E+118,53223E+127,53116E+131,2416774791,87108061,4269249770,9674272781,1184045983,6892404354,4774012192,4205647311,3786564652,6181676190,4759872811,1327931121,6933681632,372881794379822534950216507403297810082056001153019520452107111814693999317,25205E+11654524875543398700507447216920048571703557544895900961,45128E+111,75775E+128,412,698,21113,312,612,814,9319,92319,48822,81514,5719,9540581880,5390243981,9682926882,2390243983,0960975681,6487804970,0721951278,7414634180,9048780574,20967,54574,96660,0675,01371,173298993876,9481043836,6562992709,2977283537,363177733,4116555167,0103536872,0272843042,8598711413,733385599,15561981634,6974182636,6847262149,6025691891,700426
81970-01-01 00:00:00.0000020131078217831204180,4375913030,576326675-1,8410654512,0051001772,3291225061,7554221491,8420810711,8900183423,004822676,3861064011,3540919622,4854680087,7661500982,8446516333,7338E+122,81188E+122,14192E+125,21233E+121,8466E+122,29247E+121,67848E+132,80329E+122,47282E+121,85672E+121,27444E+124,00886E+119,57041E+127,74432E+131,1852579011,8498759181,3990204190,950866051,00236723,8540425834,0466788792,2936402411,3294460842,5488256790,5079194551,1232742621,7028550962,304419434424264730352001462448299574459054902393240718515731210883528964646,79229E+11638377248553287478723147403528801647314437841182084831,6407E+111,75559E+128,512,48,58,210,813,212,412,114,77219,41619,19822,48313,0319,4524478480,490243982,219512282,690243983,3319512281,7487804970,5787804978,7414634181,0048780574,48367,93174,9361,09975,32171,465864413939,5295633833,5342592579,4725433567,6293547743,7257425078,6263426905,5986332987,7005891461,076774605,79403781616,6136452602,8455982204,2432991894,112059
91970-01-01 00:00:00.000002014978818125194102,2095434310,956183052-0,0045475420,2962055142,8700360750,7362672212,5259734462,9911648140,503955747,4102276052,8497732551,4138264527,4257636563,1177528173,88909E+122,85596E+122,16201E+124,89699E+121,80575E+122,05924E+121,75272E+133,08717E+122,45604E+122,03913E+121,31535E+123,81199E+111,04757E+137,95755E+131,1499419971,8629614391,2829696540,9669993340,9899252994,1129929783,695894652,1839062481,3302444232,5439825030,5138299571,1094378831,7286890682,2510887514466283116853134750899277010343354690346661317853640478846965046536,47789E+11669954686543266023936950914096277675869384538924850911,82109E+111,75419E+128,812,48,3810,813,312,51214,62418,98418,89222,11313,8319,4641918681,090243982,719512283,090243983,5878048881,870,7436585478,8414634181,3048780574,74568,28674,90861,96875,62971,746054733779,4619213659,0877952414,4840023470,7631297897,8556154942,8754836960,6839972777,3109871495,541141636,5718341561,8733422695,5057762224,3548981919,991765

Last rows

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Italy [ITA]GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN]GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN]GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS]GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA]GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR]GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA]GDP (current US$) [NY.GDP.MKTP.CD] - India [IND]GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX]GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF]GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN]GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD]Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU]Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA]Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA]Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN]Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN]Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS]Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA]Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR]Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA]Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND]Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX]Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF]Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN]Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Germany [DEU]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - France [FRA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Italy [ITA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD]
61970-01-01 00:00:00.0000020111068715519203873,9251927052,1927006330,7073333470,0238095243,1468813724,3000291861,5508355051,4575633913,9744230795,2413150013,663007933,1685562799,5508321793,3396178863,74931E+122,86516E+122,29499E+126,23315E+121,79333E+122,04593E+121,55426E+132,67489E+122,61616E+121,82305E+121,18049E+124,58202E+117,5515E+127,36715E+131,2061503361,8914062141,4759565970,9868064931,1932918953,4330438384,8401739952,5027264021,4118511592,7044835580,4657778031,1032576761,6655760532,4120333844516321287854120871010338288049716076221384121393720864702375239517,52288E+11665695525733693620989649633815794549845854245941540781,25286E+111,75048E+128,312,79,28,31112,612,712,815,1120,49519,76623,09713,2719,8546705380,4365853782,1146341582,1878048882,5912195181,4487804969,6839024478,6414634180,9512195173,92167,1375,01158,89574,70870,884017033869,8162293847,07222828,4048913610,8121697911,5545885049,4266317029,9546012972,1530651367,188049577,99442631587,0720712716,6811732085,0830221881,477548
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